# DataRobot models
URL: https://docs.datarobot.com/en/docs/api/reference/public-api/datarobot_models.html

> Use the endpoints described below to manage DataRobot models.

# DataRobot models

Use the endpoints described below to manage DataRobot models.

## List of bias mitigated models by project ID

Operation path: `GET /api/v2/projects/{projectId}/biasMitigatedModels/`

Authentication requirements: `BearerAuth`

List of bias mitigated models for the selected project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
| parentModelId | query | string | false | Retrieve a list of mitigated models for the parent model if specified. Otherwise retrieve a list of all mitigated models for the project. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Retrieve list of mitigated models for project.",
      "items": {
        "properties": {
          "biasMitigationTechnique": {
            "description": "Method applied to perform bias mitigation.",
            "enum": [
              "preprocessingReweighing",
              "postProcessingRejectionOptionBasedClassification"
            ],
            "type": "string",
            "x-versionadded": "v2.27"
          },
          "includeBiasMitigationFeatureAsPredictorVariable": {
            "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.",
            "type": "boolean",
            "x-versionadded": "v2.27"
          },
          "modelId": {
            "description": "Mitigated model ID",
            "type": "string"
          },
          "parentModelId": {
            "description": "Parent model ID",
            "type": [
              "string",
              "null"
            ]
          },
          "protectedFeature": {
            "description": "Protected feature that will be used in a bias mitigation task to mitigate bias",
            "type": "string"
          }
        },
        "required": [
          "biasMitigationTechnique",
          "includeBiasMitigationFeatureAsPredictorVariable",
          "modelId",
          "parentModelId",
          "protectedFeature"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Returns Bias Mitigated models results. | BiasMitigatedModelsListResponse |

## Add a request by project ID

Operation path: `POST /api/v2/projects/{projectId}/biasMitigatedModels/`

Authentication requirements: `BearerAuth`

Add a request to the queue to train a model with bias mitigation applied.
If the job has been previously submitted, the request will return the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job.

### Body parameter

```
{
  "properties": {
    "biasMitigationFeature": {
      "description": "The name of the protected feature used to mitigate bias on models.",
      "minLength": 1,
      "type": "string",
      "x-versionadded": "v2.27"
    },
    "biasMitigationParentLid": {
      "description": "The ID of the model to modify with a bias-mitigation task.",
      "type": "string"
    },
    "biasMitigationTechnique": {
      "description": "Method applied to perform bias mitigation.",
      "enum": [
        "preprocessingReweighing",
        "postProcessingRejectionOptionBasedClassification"
      ],
      "type": "string",
      "x-versionadded": "v2.27"
    },
    "includeBiasMitigationFeatureAsPredictorVariable": {
      "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.",
      "type": "boolean",
      "x-versionadded": "v2.27"
    }
  },
  "required": [
    "biasMitigationFeature",
    "biasMitigationParentLid",
    "biasMitigationTechnique",
    "includeBiasMitigationFeatureAsPredictorVariable"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | BiasMitigationModelCreate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The model training request has been successfully submitted. See Location header. | None |

## Get bias mitigation data quality information by project ID

Operation path: `GET /api/v2/projects/{projectId}/biasMitigationFeatureInfo/`

Authentication requirements: `BearerAuth`

Get bias mitigation data quality information for a given projectId and featureName.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| featureName | query | string | true | Name of feature for mitigation info. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "messages": {
      "description": "List of data quality messages. The list may include reports on more than one data quality issue, if present.",
      "items": {
        "properties": {
          "additionalInfo": {
            "description": "Zero or more text strings for secondary display after user clicks for more information.",
            "items": {
              "type": "string"
            },
            "maxItems": 50,
            "type": "array"
          },
          "messageLevel": {
            "description": "Message severity level.",
            "enum": [
              "CRITICAL",
              "INFORMATIONAL",
              "NO_ISSUES",
              "WARNING"
            ],
            "type": "string"
          },
          "messageText": {
            "description": "Text for primary display in UI.",
            "maxLength": 500,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "messageLevel",
          "messageText"
        ],
        "type": "object"
      },
      "maxItems": 50,
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "messages"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Dictionary with one entry ("messages") with list of data quality information about a feature. | MessagesInfo |

## Submit a job by project ID

Operation path: `POST /api/v2/projects/{projectId}/biasMitigationFeatureInfo/{featureName}/`

Authentication requirements: `BearerAuth`

Submit a job to create bias mitigation data quality information for a given projectId and featureName.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID |
| featureName | path | string | true | Name of feature for mitigation info. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | A URI of the newly submitted job in the "Location" header. | None |

## List all blenders by project ID

Operation path: `GET /api/v2/projects/{projectId}/blenderModels/`

Authentication requirements: `BearerAuth`

List all blenders in a project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | true | This many results will be skipped. |
| limit | query | integer | true | At most this many results are returned. If 0, all results. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid].",
      "items": {
        "properties": {
          "blenderMethod": {
            "description": "Method used to blend results of underlying models.",
            "type": "string"
          },
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelIds": {
            "description": "List of models used in blender.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderMethod",
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelIds",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | A list of all of the blenders in a project. | BlenderListResponse |
| 404 | Not Found | This resource does not exist. | None |

## Create a blender by project ID

Operation path: `POST /api/v2/projects/{projectId}/blenderModels/`

Authentication requirements: `BearerAuth`

Create a blender from other models using a specified blender method. Note: Time Series projects only allow the following blender methods: "AVG", "MED", "FORECAST_DISTANCE_ENET", and "FORECAST_DISTANCE_AVG".

### Body parameter

```
{
  "properties": {
    "blenderMethod": {
      "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".",
      "enum": [
        "PLS",
        "GLM",
        "ENET",
        "AVG",
        "MED",
        "MAE",
        "MAEL1",
        "FORECAST_DISTANCE_AVG",
        "FORECAST_DISTANCE_ENET",
        "MAX",
        "MIN"
      ],
      "type": "string"
    },
    "modelIds": {
      "description": "The list of models to use in blender.",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "blenderMethod",
    "modelIds"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | BlenderCreate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Model job successfully added to queue. See the Location header. | None |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | Unable to create a blender or request is not supported in this context. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | A url that can be polled to check the status of the job. |

## Check if models can be blended by project ID

Operation path: `POST /api/v2/projects/{projectId}/blenderModels/blendCheck/`

Authentication requirements: `BearerAuth`

Check if models can be blended.

### Body parameter

```
{
  "properties": {
    "blenderMethod": {
      "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".",
      "enum": [
        "PLS",
        "GLM",
        "ENET",
        "AVG",
        "MED",
        "MAE",
        "MAEL1",
        "FORECAST_DISTANCE_AVG",
        "FORECAST_DISTANCE_ENET",
        "MAX",
        "MIN"
      ],
      "type": "string"
    },
    "modelIds": {
      "description": "The list of models to use in blender.",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "blenderMethod",
    "modelIds"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | BlenderCreate | false | none |

### Example responses

> 200 Response

```
{
  "properties": {
    "blendable": {
      "description": "If True, the models can be blended.",
      "type": "boolean"
    },
    "reason": {
      "description": "Useful info as to why a model can't be blended.",
      "type": "string"
    }
  },
  "required": [
    "blendable",
    "reason"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Information on whether models can be blended and why. | BlenderInfoRetrieveResponse |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | Unable to process request. | None |

## Retrieve a blender by project ID

Operation path: `GET /api/v2/projects/{projectId}/blenderModels/{modelId}/`

Authentication requirements: `BearerAuth`

Retrieve a blender. Blenders are a special type of models, so the response includes all attributes that would be in a response to [GET /api/v2/projects/{projectId}/models/{modelId}/][get-apiv2projectsprojectidmodelsmodelid] as well as some additional ones.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "blenderMethod": {
      "description": "Method used to blend results of underlying models.",
      "type": "string"
    },
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelIds": {
      "description": "List of models used in blender.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderMethod",
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelIds",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The blender model. | BlenderRetrieveResponse |
| 404 | Not Found | Specified blender not found. | None |

## Retrieve all existing combined models by project ID

Operation path: `GET /api/v2/projects/{projectId}/combinedModels/`

Authentication requirements: `BearerAuth`

Retrieve all existing combined models for this project.
.. note::

```
To retrieve information on the segments for a combined model, retrieve the combined model using [GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/][get-apiv2projectsprojectidcombinedmodelscombinedmodelid].
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | The number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "The list of combined models.",
      "items": {
        "properties": {
          "combinedModelId": {
            "description": "The ID of the combined model.",
            "type": "string"
          },
          "isActiveCombinedModel": {
            "default": false,
            "description": "Indicates whether this model is the active one in segmented modeling project.",
            "type": "boolean",
            "x-versionadded": "v2.29"
          },
          "modelCategory": {
            "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
            "enum": [
              "combined"
            ],
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project.",
            "type": "string"
          },
          "segmentationTaskId": {
            "description": "The ID of the segmentation task used to generate this combined model.",
            "type": "string"
          }
        },
        "required": [
          "combinedModelId",
          "isActiveCombinedModel",
          "modelCategory",
          "projectId",
          "segmentationTaskId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | CombinedModelListResponse |

## Retrieve an existing combined model by project ID

Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/`

Authentication requirements: `BearerAuth`

Retrieve an existing combined model. If available, contains information on which champion model is used for each segment.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The ID of the project. |
| combinedModelId | path | string | true | The ID of the combined model. |

### Example responses

> 200 Response

```
{
  "properties": {
    "combinedModelId": {
      "description": "The ID of the combined model.",
      "type": "string"
    },
    "isActiveCombinedModel": {
      "default": false,
      "description": "Indicates whether this model is the active one in segmented modeling project.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "modelCategory": {
      "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
      "enum": [
        "combined"
      ],
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project.",
      "type": "string"
    },
    "segmentationTaskId": {
      "description": "The ID of the segmentation task used to generate this combined model.",
      "type": "string"
    },
    "segments": {
      "description": "Information for each segment. Maps each segment to the project and model used for it.",
      "items": {
        "properties": {
          "modelId": {
            "description": "The ID of the segment champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectId": {
            "description": "The ID of the project used for this segment.",
            "type": [
              "string",
              "null"
            ]
          },
          "segment": {
            "description": "Segment name.",
            "type": "string"
          }
        },
        "required": [
          "modelId",
          "projectId",
          "segment"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "combinedModelId",
    "isActiveCombinedModel",
    "modelCategory",
    "projectId",
    "segmentationTaskId",
    "segments"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | CombinedModelResponse |

## Retrieve Combined Model segments info by project ID

Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/segments/`

Authentication requirements: `BearerAuth`

Retrieve Combined Model segments info (name, related project & model details).

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | The number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| searchSegmentName | query | string | false | Case-insensitive search against segment name. |
| projectId | path | string | true | The ID of the project. |
| combinedModelId | path | string | true | The ID of the combined model. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "The list of combined model segment info.",
      "items": {
        "properties": {
          "autopilotDone": {
            "description": "Whether autopilot is done for the project.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "holdoutUnlocked": {
            "description": "Whether holdout is unlocked for the project.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "isFrozen": {
            "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "modelAssignedBy": {
            "description": "Who assigned the model as segment champion. The default is ``DataRobot``.",
            "type": [
              "string",
              "null"
            ]
          },
          "modelAwardTime": {
            "description": "The time when the model was awarded as segment champion.",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "modelCount": {
            "description": "The count of trained models in the project.",
            "type": [
              "integer",
              "null"
            ]
          },
          "modelIcon": {
            "description": "The number for the icon representing the given champion model.",
            "items": {
              "type": "integer"
            },
            "type": "array"
          },
          "modelId": {
            "description": "The ID of the segment champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "modelMetrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.",
            "type": [
              "object",
              "null"
            ]
          },
          "modelType": {
            "description": "The description of the model type of the given champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectId": {
            "description": "The ID of the project.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectPaused": {
            "description": "Whether the project is paused right now.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "projectStage": {
            "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.",
            "enum": [
              "modeling",
              "aim",
              "fasteda",
              "eda",
              "eda2",
              "empty"
            ],
            "type": [
              "string",
              "null"
            ]
          },
          "projectStageDescription": {
            "description": "A description of the current stage of the project.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectStatusError": {
            "description": "Project status error message.",
            "type": [
              "string",
              "null"
            ]
          },
          "rowCount": {
            "description": "The count of rows in the project's dataset.",
            "type": [
              "integer",
              "null"
            ]
          },
          "rowPercentage": {
            "description": "The percentage of rows in the segment project's dataset compared to the original dataset.",
            "type": [
              "number",
              "null"
            ]
          },
          "segment": {
            "description": "Segment name.",
            "type": "string"
          }
        },
        "required": [
          "autopilotDone",
          "holdoutUnlocked",
          "isFrozen",
          "modelAssignedBy",
          "modelAwardTime",
          "modelCount",
          "modelIcon",
          "modelId",
          "modelMetrics",
          "modelType",
          "projectId",
          "projectStage",
          "projectStageDescription",
          "rowCount",
          "rowPercentage",
          "segment"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | CombinedModelSegmentsPaginatedResponse |

## Download Combined Model segments info by project ID

Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/segments/download/`

Authentication requirements: `BearerAuth`

Download Combined Model segments info (name, related project & model details) as a CSV.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The ID of the project. |
| combinedModelId | path | string | true | The ID of the combined model. |

### Example responses

> 200 Response

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | string |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Content-Disposition | string |  | Contains an auto-generated filename for this download. |

## List datetime partitioned project models by project ID

Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/`

Authentication requirements: `BearerAuth`

List all the models from a datetime partitioned project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | The number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| bulkOperationId | query | string | false | the ID of the bulk model operation. If specified, only models submitted in scope of this operation will be shown. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid].",
      "items": {
        "properties": {
          "backtests": {
            "description": "information on each backtesting fold of the model",
            "items": {
              "properties": {
                "index": {
                  "description": "the index of the fold",
                  "type": "integer"
                },
                "score": {
                  "description": "the score of the model for this backtesting fold, if computed",
                  "type": [
                    "number",
                    "null"
                  ]
                },
                "status": {
                  "description": "the status of the current backtest model job",
                  "enum": [
                    "COMPLETED",
                    "NOT_COMPLETED",
                    "INSUFFICIENT_DATA",
                    "ERRORED",
                    "BACKTEST_BOUNDARIES_EXCEEDED"
                  ],
                  "type": "string"
                },
                "trainingDuration": {
                  "description": "the duration of the data used to train the model for this backtesting fold",
                  "format": "duration",
                  "type": "string"
                },
                "trainingEndDate": {
                  "description": "the end date of the training for this backtesting fold",
                  "format": "date-time",
                  "type": "string"
                },
                "trainingRowCount": {
                  "description": "the number of rows used to train the model for this backtesting fold",
                  "type": "integer"
                },
                "trainingStartDate": {
                  "description": "the start date of the training for this backtesting fold",
                  "format": "date-time",
                  "type": "string"
                }
              },
              "required": [
                "index",
                "score",
                "status",
                "trainingDuration",
                "trainingEndDate",
                "trainingRowCount",
                "trainingStartDate"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "effectiveFeatureDerivationWindowEnd": {
            "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.",
            "maximum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "effectiveFeatureDerivationWindowStart": {
            "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.",
            "exclusiveMaximum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "forecastWindowEnd": {
            "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.",
            "minimum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "forecastWindowStart": {
            "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.",
            "minimum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "holdoutScore": {
            "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked",
            "type": [
              "number",
              "null"
            ]
          },
          "holdoutStatus": {
            "description": "the status of the holdout fold",
            "enum": [
              "COMPLETED",
              "INSUFFICIENT_DATA",
              "HOLDOUT_BOUNDARIES_EXCEEDED"
            ],
            "type": "string"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training.  The `backtesting` and  `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.",
            "example": "\n        {\n            \"metrics\": {\n                \"FVE Poisson\": {\n                    \"holdout\": null,\n                    \"validation\": 0.56269,\n                    \"backtesting\": 0.50166,\n                    \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n                    \"crossValidation\": null\n                },\n                \"RMSE\": {\n                    \"holdout\": null,\n                    \"validation\": 21.0836,\n                    \"backtesting\": 23.361932,\n                    \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n                    \"crossValidation\": null\n                }\n            }\n        }\n",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "always null for datetime models",
            "enum": [
              null
            ],
            "type": "string"
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingInfo": {
            "description": "holdout and prediction training data details",
            "properties": {
              "holdoutTrainingDuration": {
                "description": "the duration of the data used to train a model to score the holdout",
                "format": "duration",
                "type": "string"
              },
              "holdoutTrainingEndDate": {
                "description": "the end date of the data used to train a model to score the holdout",
                "format": "date-time",
                "type": "string"
              },
              "holdoutTrainingRowCount": {
                "description": "the number of rows used to train a model to score the holdout",
                "type": "integer"
              },
              "holdoutTrainingStartDate": {
                "description": "the start date of data used to train a model to score the holdout",
                "format": "date-time",
                "type": "string"
              },
              "predictionTrainingDuration": {
                "description": "the duration of the data used to train a model to make predictions",
                "format": "duration",
                "type": "string"
              },
              "predictionTrainingEndDate": {
                "description": "the end date of the data used to train a model to make predictions",
                "format": "date-time",
                "type": "string"
              },
              "predictionTrainingRowCount": {
                "description": "the number of rows used to train a model to make predictions",
                "type": "integer"
              },
              "predictionTrainingStartDate": {
                "description": "the start date of data used to train a model to make predictions",
                "format": "date-time",
                "type": "string"
              }
            },
            "required": [
              "holdoutTrainingDuration",
              "holdoutTrainingEndDate",
              "holdoutTrainingRowCount",
              "holdoutTrainingStartDate",
              "predictionTrainingDuration",
              "predictionTrainingEndDate",
              "predictionTrainingRowCount",
              "predictionTrainingStartDate"
            ],
            "type": "object"
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "windowsBasisUnit": {
            "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.",
            "enum": [
              "MILLISECOND",
              "SECOND",
              "MINUTE",
              "HOUR",
              "DAY",
              "WEEK",
              "MONTH",
              "QUARTER",
              "YEAR",
              "ROW"
            ],
            "type": "string",
            "x-versionadded": "v2.16"
          }
        },
        "required": [
          "backtests",
          "blenderModels",
          "blueprintId",
          "effectiveFeatureDerivationWindowEnd",
          "effectiveFeatureDerivationWindowStart",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "forecastWindowEnd",
          "forecastWindowStart",
          "frozenPct",
          "hasCodegen",
          "holdoutScore",
          "holdoutStatus",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingInfo",
          "trainingRowCount",
          "trainingStartDate",
          "windowsBasisUnit"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The datetime partitioned project's models | DatetimeModelsResponse |

## Train a new datetime model by project ID

Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/`

Authentication requirements: `BearerAuth`

Train a new datetime model.

All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules<time_format>`.

### Body parameter

```
{
  "properties": {
    "blueprintId": {
      "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.",
      "type": "string"
    },
    "featurelistId": {
      "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.18"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.18"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string",
      "x-versionadded": "v2.20"
    },
    "sourceProjectId": {
      "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.",
      "type": "string"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer",
      "x-versionadded": "v2.7"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration for the submitted model.",
      "format": "duration",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used when training this model.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    }
  },
  "required": [
    "blueprintId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | TrainDatetimeModel | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The model has been successfully submitted. | DatetimeModelSubmissionResponse |
| 422 | Unprocessable Entity | There was an error submitting the specified job. See the message field for more details. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Retrain an existing datetime model by project ID

Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/fromModel/`

Authentication requirements: `BearerAuth`

Retrain an existing datetime model using a new training period for the model training set (with optional time window sampling) or different feature list.

All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules<time_format>`.

Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.

### Body parameter

```
{
  "properties": {
    "featurelistId": {
      "description": "If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model.",
      "type": "string"
    },
    "modelId": {
      "description": "The ID of an existing model to use as the source for the training parameters.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string",
      "x-versionadded": "v2.20"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "duration",
      "type": "string"
    },
    "trainingEndDate": {
      "description": "A datetime string representing the end date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.",
      "format": "date-time",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingStartDate": {
      "description": "A datetime string representing the start date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.",
      "format": "date-time",
      "type": "string"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "type": "boolean",
      "x-versionadded": "v2.24"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | RetrainDatetimeModel | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Retrain an existing datetime model using a new sample size and/or feature list. | DatetimeModelSubmissionResponse |
| 403 | Forbidden | User does not have permissions to manage models. | None |
| 404 | Not Found | Model with specified modelId doesn't exist, or user does not have access to the project. | None |
| 422 | Unprocessable Entity | Model with specified modelId is deprecated, or it doesn't support retraining with specified parameters. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Get datetime model by project ID

Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/`

Authentication requirements: `BearerAuth`

Look up a particular datetime model
All durations and datetimes are specified in accordance with :ref: `timestamp and duration formatting rules <time_format>`.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "backtests": {
      "description": "information on each backtesting fold of the model",
      "items": {
        "properties": {
          "index": {
            "description": "the index of the fold",
            "type": "integer"
          },
          "score": {
            "description": "the score of the model for this backtesting fold, if computed",
            "type": [
              "number",
              "null"
            ]
          },
          "status": {
            "description": "the status of the current backtest model job",
            "enum": [
              "COMPLETED",
              "NOT_COMPLETED",
              "INSUFFICIENT_DATA",
              "ERRORED",
              "BACKTEST_BOUNDARIES_EXCEEDED"
            ],
            "type": "string"
          },
          "trainingDuration": {
            "description": "the duration of the data used to train the model for this backtesting fold",
            "format": "duration",
            "type": "string"
          },
          "trainingEndDate": {
            "description": "the end date of the training for this backtesting fold",
            "format": "date-time",
            "type": "string"
          },
          "trainingRowCount": {
            "description": "the number of rows used to train the model for this backtesting fold",
            "type": "integer"
          },
          "trainingStartDate": {
            "description": "the start date of the training for this backtesting fold",
            "format": "date-time",
            "type": "string"
          }
        },
        "required": [
          "index",
          "score",
          "status",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "effectiveFeatureDerivationWindowEnd": {
      "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.",
      "maximum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "effectiveFeatureDerivationWindowStart": {
      "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.",
      "exclusiveMaximum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "forecastWindowEnd": {
      "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "forecastWindowStart": {
      "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "holdoutScore": {
      "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked",
      "type": [
        "number",
        "null"
      ]
    },
    "holdoutStatus": {
      "description": "the status of the holdout fold",
      "enum": [
        "COMPLETED",
        "INSUFFICIENT_DATA",
        "HOLDOUT_BOUNDARIES_EXCEEDED"
      ],
      "type": "string"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training.  The `backtesting` and  `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.",
      "example": "\n        {\n            \"metrics\": {\n                \"FVE Poisson\": {\n                    \"holdout\": null,\n                    \"validation\": 0.56269,\n                    \"backtesting\": 0.50166,\n                    \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n                    \"crossValidation\": null\n                },\n                \"RMSE\": {\n                    \"holdout\": null,\n                    \"validation\": 21.0836,\n                    \"backtesting\": 23.361932,\n                    \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n                    \"crossValidation\": null\n                }\n            }\n        }\n",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "always null for datetime models",
      "enum": [
        null
      ],
      "type": "string"
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingInfo": {
      "description": "holdout and prediction training data details",
      "properties": {
        "holdoutTrainingDuration": {
          "description": "the duration of the data used to train a model to score the holdout",
          "format": "duration",
          "type": "string"
        },
        "holdoutTrainingEndDate": {
          "description": "the end date of the data used to train a model to score the holdout",
          "format": "date-time",
          "type": "string"
        },
        "holdoutTrainingRowCount": {
          "description": "the number of rows used to train a model to score the holdout",
          "type": "integer"
        },
        "holdoutTrainingStartDate": {
          "description": "the start date of data used to train a model to score the holdout",
          "format": "date-time",
          "type": "string"
        },
        "predictionTrainingDuration": {
          "description": "the duration of the data used to train a model to make predictions",
          "format": "duration",
          "type": "string"
        },
        "predictionTrainingEndDate": {
          "description": "the end date of the data used to train a model to make predictions",
          "format": "date-time",
          "type": "string"
        },
        "predictionTrainingRowCount": {
          "description": "the number of rows used to train a model to make predictions",
          "type": "integer"
        },
        "predictionTrainingStartDate": {
          "description": "the start date of data used to train a model to make predictions",
          "format": "date-time",
          "type": "string"
        }
      },
      "required": [
        "holdoutTrainingDuration",
        "holdoutTrainingEndDate",
        "holdoutTrainingRowCount",
        "holdoutTrainingStartDate",
        "predictionTrainingDuration",
        "predictionTrainingEndDate",
        "predictionTrainingRowCount",
        "predictionTrainingStartDate"
      ],
      "type": "object"
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "windowsBasisUnit": {
      "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.",
      "enum": [
        "MILLISECOND",
        "SECOND",
        "MINUTE",
        "HOUR",
        "DAY",
        "WEEK",
        "MONTH",
        "QUARTER",
        "YEAR",
        "ROW"
      ],
      "type": "string",
      "x-versionadded": "v2.16"
    }
  },
  "required": [
    "backtests",
    "blenderModels",
    "blueprintId",
    "effectiveFeatureDerivationWindowEnd",
    "effectiveFeatureDerivationWindowStart",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "forecastWindowEnd",
    "forecastWindowStart",
    "frozenPct",
    "hasCodegen",
    "holdoutScore",
    "holdoutStatus",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingInfo",
    "trainingRowCount",
    "trainingStartDate",
    "windowsBasisUnit"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Datetime model | DatetimeModelDetailsResponse |

## Score all the available backtests of a datetime model by project ID

Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/`

Authentication requirements: `BearerAuth`

Score all the available backtests of a datetime model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Scoring of all the available backtests of a datetime model has been successfully requested. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Prepare a model by project ID

Operation path: `POST /api/v2/projects/{projectId}/deploymentReadyModels/`

Authentication requirements: `BearerAuth`

Prepare a specific model for deployment. This model will go through the recommendation stages.

### Body parameter

```
{
  "properties": {
    "modelId": {
      "description": "The model to prepare for deployment.",
      "type": "string"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | PrepareForDeployment | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 201 | Created | Start preparing the model for deployment. | None |
| 422 | Unprocessable Entity | An error occurred when submitting the model job | None |

## Retrieve Eureqa model details plot by project ID

Operation path: `GET /api/v2/projects/{projectId}/eureqaDistributionPlot/{solutionId}/`

Authentication requirements: `BearerAuth`

Retrieve Eureqa model details plot.

Available for classification projects only.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| solutionId | path | string | true | The ID of the solution to return data for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "bins": {
      "description": "The distribution plot data.",
      "items": {
        "properties": {
          "binEnd": {
            "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.",
            "type": "number"
          },
          "binStart": {
            "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.",
            "type": "number"
          },
          "negatives": {
            "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.",
            "type": "integer"
          },
          "positives": {
            "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.",
            "type": "integer"
          }
        },
        "required": [
          "binEnd",
          "binStart",
          "negatives",
          "positives"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "complexity": {
      "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
      "type": "integer"
    },
    "error": {
      "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
      "type": [
        "number",
        "null"
      ]
    },
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "eureqaSolutionId": {
      "description": "The ID of the solution.",
      "type": "string"
    },
    "expression": {
      "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
      "type": "string"
    },
    "expressionAnnotated": {
      "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
      "type": "string"
    },
    "threshold": {
      "description": "Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the `binStart` of the first bin and `binEnd` of the last bin.",
      "type": "number"
    }
  },
  "required": [
    "bins",
    "complexity",
    "error",
    "errorMetric",
    "eureqaSolutionId",
    "expression",
    "expressionAnnotated",
    "threshold"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The Eureqa model details plot. | EureqaDistributionDetailResponse |
| 404 | Not Found | Data was not found. | None |

## Retrieve eureqa model detail by id

Operation path: `GET /api/v2/projects/{projectId}/eureqaModelDetail/{solutionId}/`

Authentication requirements: `BearerAuth`

Retrieve Eureqa model details plot.

Available for regression projects only.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| solutionId | path | string | true | The ID of the solution to return data for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "complexity": {
      "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
      "type": "integer"
    },
    "error": {
      "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
      "type": [
        "number",
        "null"
      ]
    },
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "eureqaSolutionId": {
      "description": "The ID of the solution.",
      "type": "string"
    },
    "expression": {
      "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
      "type": "string"
    },
    "expressionAnnotated": {
      "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
      "type": "string"
    },
    "plotData": {
      "description": "The plot data.",
      "items": {
        "properties": {
          "actual": {
            "description": "The actual value of the target variable for the specified row.",
            "type": "number"
          },
          "predicted": {
            "description": "The predicted value of the target by the solution for the specified row.",
            "type": "number"
          },
          "row": {
            "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.",
            "type": "integer"
          }
        },
        "required": [
          "actual",
          "predicted",
          "row"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "complexity",
    "error",
    "errorMetric",
    "eureqaSolutionId",
    "expression",
    "expressionAnnotated",
    "plotData"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The Eureqa model details plot. | EureqaModelDetailResponse |
| 404 | Not Found | Data was not found. | None |

## Create a new model by project ID

Operation path: `POST /api/v2/projects/{projectId}/eureqaModels/`

Authentication requirements: `BearerAuth`

Create a new model from an existing eureqa solution.

### Body parameter

```
{
  "properties": {
    "parentModelId": {
      "description": "The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with `solutionId`.",
      "type": "string"
    },
    "solutionId": {
      "description": "The ID of the solution to be cloned.",
      "type": "string"
    }
  },
  "required": [
    "solutionId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | EureqaLeaderboardEntryPayload | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Request accepted, creation is underway. | None |
| 404 | Not Found | Data not found. | None |
| 422 | Unprocessable Entity | Model for this solution already exists. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Location | string |  | The location at which the new model can be retrieved. |

## Retrieve the pareto front by project ID

Operation path: `GET /api/v2/projects/{projectId}/eureqaModels/{modelId}/`

Authentication requirements: `BearerAuth`

Retrieve the pareto front for the specified Eureqa model.

Only the best solution in the pareto front will have a corresponding model initially. Models can be created for other solutions using [POST /api/v2/projects/{projectId}/eureqaModels/][post-apiv2projectsprojectideureqamodels].

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "hyperparameters": {
      "description": "The hyperparameters used by this run of the Eureqa blueprint.",
      "oneOf": [
        {
          "properties": {
            "buildingBlocks": {
              "description": "Mathematical operators and other components that comprise Eureqa Expressions.",
              "type": [
                "object",
                "null"
              ]
            },
            "errorMetric": {
              "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ",
              "type": [
                "string",
                "null"
              ]
            },
            "maxGenerations": {
              "description": "The maximum number of evolutionary generations to run.",
              "minimum": 32,
              "type": [
                "integer",
                "null"
              ]
            },
            "numThreads": {
              "description": "The number of threads Eureqa will run with.",
              "minimum": 0,
              "type": [
                "integer",
                "null"
              ]
            },
            "priorSolutions": {
              "description": "Prior Eureqa Solutions.",
              "items": {
                "description": "Prior solution.",
                "type": "string"
              },
              "type": "array"
            },
            "randomSeed": {
              "description": "Constant to seed Eureqa's pseudo-random number generator.",
              "minimum": 0,
              "type": [
                "integer",
                "null"
              ]
            },
            "splitMode": {
              "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.",
              "enum": [
                "custom",
                "1",
                "2"
              ],
              "type": [
                "string",
                "null"
              ]
            },
            "syncMigrations": {
              "description": "Whether Eureqa's migrations are synchronized.",
              "type": [
                "boolean",
                "null"
              ]
            },
            "targetExpressionFormat": {
              "description": "Constrain the target expression to the specified format.",
              "enum": [
                "None",
                "exponential",
                "featureInteraction"
              ],
              "type": [
                "string",
                "null"
              ]
            },
            "targetExpressionString": {
              "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.",
              "type": [
                "string",
                "null"
              ]
            },
            "timeoutSec": {
              "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.",
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "trainingFraction": {
              "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.",
              "maximum": 1,
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "trainingSplitExpr": {
              "description": "Valid Eureqa Expression to do Eureqa internal training splits.",
              "type": [
                "string",
                "null"
              ]
            },
            "validationFraction": {
              "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.",
              "maximum": 1,
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "validationSplitExpr": {
              "description": "Valid Eureqa Expression to do Eureqa internal validation splits.",
              "type": [
                "string",
                "null"
              ]
            },
            "weightExpr": {
              "description": "Eureqa Weight Expression.",
              "type": [
                "string",
                "null"
              ]
            }
          },
          "required": [
            "buildingBlocks",
            "maxGenerations",
            "numThreads",
            "priorSolutions",
            "randomSeed",
            "splitMode",
            "syncMigrations",
            "targetExpressionFormat",
            "targetExpressionString",
            "timeoutSec",
            "trainingFraction",
            "trainingSplitExpr",
            "validationFraction",
            "validationSplitExpr",
            "weightExpr"
          ],
          "type": "object"
        },
        {
          "type": "null"
        }
      ]
    },
    "projectId": {
      "description": "The project ID of the Eureqa model.",
      "type": "string"
    },
    "solutions": {
      "description": "The Eureqa model solutions.",
      "items": {
        "properties": {
          "bestModel": {
            "description": "True if this solution generates the best model.",
            "type": "boolean"
          },
          "complexity": {
            "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
            "type": "integer"
          },
          "error": {
            "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
            "type": [
              "number",
              "null"
            ]
          },
          "eureqaSolutionId": {
            "description": "The ID of the solution.",
            "type": "string"
          },
          "expression": {
            "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
            "type": "string"
          },
          "expressionAnnotated": {
            "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
            "type": "string"
          }
        },
        "required": [
          "bestModel",
          "complexity",
          "error",
          "eureqaSolutionId",
          "expression",
          "expressionAnnotated"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "targetType": {
      "description": "The type of the target variable.",
      "enum": [
        "Regression",
        "Binary"
      ],
      "type": "string"
    }
  },
  "required": [
    "errorMetric",
    "hyperparameters",
    "projectId",
    "solutions",
    "targetType"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The Pareto front for the Eureqa model. | ParetoFrontResponse |
| 404 | Not Found | Data was not found. | None |

## Train a frozen datetime model by project ID

Operation path: `POST /api/v2/projects/{projectId}/frozenDatetimeModels/`

Authentication requirements: `BearerAuth`

Train a frozen datetime model. If no training data is specified, the frozen datetime model will be trained on the most recent data using an amount of data that is equivalent to the original model. However, if the equivalent duration does not provide enough rows for training, then the duration will be extended until the minimum is met. Note that this will require the holdout of the project to be unlocked.

All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules<time_format>`.

Note that only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.

### Body parameter

```
{
  "properties": {
    "modelId": {
      "description": "The ID of an existing model to use as the source for the training parameters.",
      "type": "string"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration for the submitted model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "duration",
      "type": "string"
    },
    "trainingEndDate": {
      "description": "A datetime string representing the end date of the data to use for training this model. If specified, `trainingStartDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "date-time",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used when training this model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingStartDate": {
      "description": "A datetime string representing the start date of the data to use for training this model. If specified, `trainingEndDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "date-time",
      "type": "string"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "type": "boolean",
      "x-versionadded": "v2.24"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | TrainDatetimeFrozenModel | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The model has been successfully submitted. | DatetimeModelSubmissionResponse |
| 403 | Forbidden | User does not have permissions to manage models. | None |
| 404 | Not Found | Model with specified modelId does not exist, or user does not have access to the project. | None |
| 422 | Unprocessable Entity | Model with specified modelId is deprecated, or it does not support retraining with specified parameters. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## List all frozen models by project ID

Operation path: `GET /api/v2/projects/{projectId}/frozenModels/`

Authentication requirements: `BearerAuth`

List all frozen models from a project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | true | This many results will be skipped. |
| limit | query | integer | true | At most this many results are returned. If 0, all results. |
| withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "An array of the frozen models in a project.",
      "items": {
        "properties": {
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The list of frozen models in the project. | FrozenModelListResponse |
| 404 | Not Found | This resource does not exist. | None |

## Train a new frozen model by project ID

Operation path: `POST /api/v2/projects/{projectId}/frozenModels/`

Authentication requirements: `BearerAuth`

Train a new frozen model with parameters from an existing model. Frozen models use tuning parameters from another model on the leaderboard, allowing them to be retrained on a larger amount of the training data more efficiently. 
 To specify the amount of data to use to train the model, use either `samplePct` to express a percentage of the rows of the dataset to use or `trainingRowCount` to express the number of rows to use. 
 If neither `samplePct` or `trainingRowCount` is specified, the model will be trained on the maximum available training data that can be used to train an in-memory model. 
 For projects using smart sampling, `samplePct` and `trainingRowCount` will be interpreted as a percent or number of rows of the minority class. 
 When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use.

### Body parameter

```
{
  "properties": {
    "modelId": {
      "description": "The ID of an existing model to use as a source of training parameters.",
      "type": "string"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0.0 and 100.0.",
      "type": "number"
    },
    "trainingRowCount": {
      "description": "The integer number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.",
      "type": "integer"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | FrozenModelCreate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 204 | No Content | The frozen model has been successfully submitted. | None |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | Unable to process request. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 204 | Location | string | url | Contains a url at which the job processing the model can be retrieved as with [GET /api/v2/projects/{projectId}/modelJobs/{jobId}/][get-apiv2projectsprojectidmodeljobsjobid].. |

## Look up a particular frozen model by project ID

Operation path: `GET /api/v2/projects/{projectId}/frozenModels/{modelId}/`

Authentication requirements: `BearerAuth`

Look up a particular frozen model. If model with given ID exists but it's not frozen, returns 404 Not Found.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The frozen model. | ModelDetailsResponse |
| 404 | Not Found | No such frozen model found. | None |

## Train a new incremental learning model based on an existing model and external data, that was not by project ID

Operation path: `POST /api/v2/projects/{projectId}/incrementalLearningModels/fromModel/`

Authentication requirements: `BearerAuth`

Train a new incremental learning model based on an existing model and external data, that was not seen during partitioning.

### Body parameter

```
{
  "properties": {
    "dataStageCompression": {
      "description": "The file compression for the data stage. Only supports CSV storage type.",
      "enum": [
        "zip",
        "bz2",
        "gzip"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageDelimiter": {
      "description": "The file delimiter for the data stage specified as a string.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageEncoding": {
      "description": "The file encoding for the data stage.",
      "enum": [
        "UTF-8",
        "ASCII",
        "WINDOWS-1252"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageId": {
      "description": "The data stage ID. The data stage must be finalized and not expired.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageStorageType": {
      "default": "csv",
      "description": "The file type of the data stage contents.",
      "enum": [
        "csv",
        "parquet"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelId": {
      "description": "The parent model ID. This model must support incremental learning.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "trainingDataName": {
      "description": "String identifier for the current iteration.",
      "maxLength": 500,
      "type": "string",
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "dataStageId",
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | TrainIncrementalLearningModel | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The incremental learning model submission response. | ModelRetrainResponse |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## List modeling jobs by project ID

Operation path: `GET /api/v2/projects/{projectId}/modelJobs/`

Authentication requirements: `BearerAuth`

List modeling jobs.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| status | query | string | false | If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned. |
| projectId | path | string | true | The project ID. |

### Enumerated Values

| Parameter | Value |
| --- | --- |
| status | [queue, inprogress, error] |

### Example responses

> 200 Response

```
{
  "items": {
    "properties": {
      "data": {
        "description": "List of modeling jobs.",
        "items": {
          "properties": {
            "blueprintId": {
              "description": "The blueprint used by the model - note that this is not an ObjectId.",
              "type": "string"
            },
            "featurelistId": {
              "description": "The ID of the featurelist the model is using.",
              "type": "string"
            },
            "id": {
              "description": "The job ID.",
              "type": "string"
            },
            "isBlocked": {
              "description": "True if a job is waiting for its dependencies to be resolved first.",
              "type": "boolean"
            },
            "isTrainedOnGpu": {
              "description": "Whether the job was trained using GPU capabilities.",
              "type": "boolean"
            },
            "modelCategory": {
              "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
              "enum": [
                "model",
                "prime",
                "blend"
              ],
              "type": "string"
            },
            "modelId": {
              "description": "The ID of the model.",
              "type": "string"
            },
            "modelType": {
              "description": "The type of model used by the job.",
              "type": "string"
            },
            "processes": {
              "description": "The list of processes the modeling job includes.",
              "items": {
                "type": "string"
              },
              "type": "array"
            },
            "projectId": {
              "description": "The project the job belongs to.",
              "type": "string"
            },
            "samplePct": {
              "description": "The percentage of the dataset the job is using.",
              "type": "number"
            },
            "status": {
              "description": "The status of the job.",
              "enum": [
                "queue",
                "inprogress",
                "error",
                "ABORTED",
                "COMPLETED"
              ],
              "type": "string"
            }
          },
          "required": [
            "blueprintId",
            "featurelistId",
            "id",
            "isBlocked",
            "modelCategory",
            "modelId",
            "modelType",
            "processes",
            "projectId",
            "status"
          ],
          "type": "object"
        },
        "type": "array"
      }
    },
    "required": [
      "data"
    ],
    "type": "object"
  },
  "type": "array"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The list of modeling jobs. | Inline |

### Response Schema

Status Code 200

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| anonymous | [ModelingJobListResponse] | false |  | none |
| » data | [ModelJobResponse] | true |  | List of modeling jobs. |
| »» blueprintId | string | true |  | The blueprint used by the model - note that this is not an ObjectId. |
| »» featurelistId | string | true |  | The ID of the featurelist the model is using. |
| »» id | string | true |  | The job ID. |
| »» isBlocked | boolean | true |  | True if a job is waiting for its dependencies to be resolved first. |
| »» isTrainedOnGpu | boolean | false |  | Whether the job was trained using GPU capabilities. |
| »» modelCategory | string | true |  | Indicates what kind of model this is. Will be combined for combined models. |
| »» modelId | string | true |  | The ID of the model. |
| »» modelType | string | true |  | The type of model used by the job. |
| »» processes | [string] | true |  | The list of processes the modeling job includes. |
| »» projectId | string | true |  | The project the job belongs to. |
| »» samplePct | number | false |  | The percentage of the dataset the job is using. |
| »» status | string | true |  | The status of the job. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | [model, prime, blend] |
| status | [queue, inprogress, error, ABORTED, COMPLETED] |

## Cancel a modeling job by project ID

Operation path: `DELETE /api/v2/projects/{projectId}/modelJobs/{jobId}/`

Authentication requirements: `BearerAuth`

Cancel a modeling job.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| jobId | path | string | true | The job ID. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 204 | No Content | The job has been successfully cancelled. | None |
| 404 | Not Found | no job with jobId found, or the job has already completed | None |

## Look up a specific modeling job by project ID

Operation path: `GET /api/v2/projects/{projectId}/modelJobs/{jobId}/`

Authentication requirements: `BearerAuth`

Look up a particular modeling job.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| jobId | path | string | true | The job ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "blueprintId": {
      "description": "The blueprint used by the model - note that this is not an ObjectId.",
      "type": "string"
    },
    "featurelistId": {
      "description": "The ID of the featurelist the model is using.",
      "type": "string"
    },
    "id": {
      "description": "The job ID.",
      "type": "string"
    },
    "isBlocked": {
      "description": "True if a job is waiting for its dependencies to be resolved first.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the job was trained using GPU capabilities.",
      "type": "boolean"
    },
    "modelCategory": {
      "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
      "enum": [
        "model",
        "prime",
        "blend"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "modelType": {
      "description": "The type of model used by the job.",
      "type": "string"
    },
    "processes": {
      "description": "The list of processes the modeling job includes.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "projectId": {
      "description": "The project the job belongs to.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset the job is using.",
      "type": "number"
    },
    "status": {
      "description": "The status of the job.",
      "enum": [
        "queue",
        "inprogress",
        "error",
        "ABORTED",
        "COMPLETED"
      ],
      "type": "string"
    }
  },
  "required": [
    "blueprintId",
    "featurelistId",
    "id",
    "isBlocked",
    "modelCategory",
    "modelId",
    "modelType",
    "processes",
    "projectId",
    "status"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The modeling job. | ModelJobResponse |
| 303 | See Other | Task is completed, see Location header for the location of a new resource | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Location | string | url | if a status code 303 was returned, will contain a url at which the completed model can be retrieved` |

## Retrieve model records, supports filtering by project ID

Operation path: `GET /api/v2/projects/{projectId}/modelRecords/`

Authentication requirements: `BearerAuth`

Retrieve model records, supports filtering and search.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. |
| showInSampleScores | query | boolean | false | If specified, will return metric scores for models trained into validation/holdout for projects that do not have stacked predictions. |
| characteristics | query | array[string] | false | A characteristics to filter models with. Only those models which have all specified characteristics are returned. |
| searchTerm | query | string | false | Filter models by the string expression in the description, case insensitive. |
| labels | query | array[string] | false | Filter models by labels. Only those models which have all specified labels are returned. |
| blueprints | query | string | false | Filter models by blueprint IDs. |
| families | query | array[string] | false | Filter models by families. |
| featurelists | query | string | false | Filter models by featurelist names. |
| trainingFilters | query | string | false | Filter models by training length or type. Could be training duration in string representation, 'Start/end date', 'Project settings' constants or number of rows. Training duration for datetime partitioned projects may have up to three parts: --.Example of the training duration: P6Y0M0D-78-Random, returns models trained on6 years, with sampling rate 78%, randomly taken from the training window. Example of the row count with sampling method: 150-Random |
| numberOfClusters | query | string | false | Filter models by number of clusters. Applicable only in unsupervised clustering projects. |
| sortByMetric | query | string | false | Metric to order models by. If omitted, the project metric is used. |
| sortByPartition | query | string | false | Partition to use when ordering models by metric. If omitted, the validation partition is used. |
| offset | query | integer | true | The number of results that are skipped. |
| limit | query | integer | true | The maximum number of models to return. |
| projectId | path | string | true | The project ID. |

### Enumerated Values

| Parameter | Value |
| --- | --- |
| characteristics | [frozen, new series optimized, trained on gpu, with exportable coefficients, with mono constraints, with rating table, with scoring code] |
| labels | [prepared for deployment, starred] |
| families | [Adaptive Boosting, Anomaly Detection, Blender, Calibrator, Clustering, Custom, Decision Tree, Document, Naive, ElasticNet Generalized Linear Model, Eureqa, Eureqa Time-Series, External Prediction Model, Factorization Machine, Generalized Additive Model, Generalized Linear Model, Gradient Boosting Machine, Image, K Nearest Neighbors, Naive Bayes, Neural Network, Other, Random Forest, Rule Induction, Summarized Categorical, Support Vector Machine, Text Mining, Time-Series Neural Network, Traditional Time-Series, Two Stage, Vowpal Wabbit] |
| sortByPartition | [backtesting, crossValidation, validation, holdout] |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of models returned on this page.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.33"
    },
    "data": {
      "description": "Model records.",
      "items": {
        "oneOf": [
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object",
                "x-versionadded": "v2.33"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.33"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingRowCount"
            ],
            "type": "object"
          },
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "dataSelectionMethod": {
                "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
                "enum": [
                  "duration",
                  "rowCount",
                  "selectedDateRange",
                  "useProjectSettings"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object",
                "x-versionadded": "v2.33"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.33"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "samplingMethod": {
                "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
                "enum": [
                  "random",
                  "latest"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "timeWindowSamplePct": {
                "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
                "exclusiveMaximum": 100,
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingDuration": {
                "description": "the duration spanned by the dates in the partition column for the data used to train the model",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingEndDate": {
                "description": "the end date of the dates in the partition column for the data used to train the model",
                "format": "date-time",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingStartDate": {
                "description": "the start date of the dates in the partition column for the data used to train the model",
                "format": "date-time",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingDuration",
              "trainingEndDate",
              "trainingRowCount",
              "trainingStartDate"
            ],
            "type": "object"
          },
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ]
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string"
              },
              "numberOfClusters": {
                "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.34"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ]
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ]
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "numberOfClusters",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingRowCount"
            ],
            "type": "object",
            "x-versionadded": "v2.34"
          }
        ]
      },
      "type": "array",
      "x-versionadded": "v2.33"
    },
    "next": {
      "description": "A URL pointing to the next page (if `null`, there is no next page).",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "previous": {
      "description": "A URL pointing to the previous page (if `null`, there is no previous page).",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "totalCount": {
      "description": "Total number of models after filters applied.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | ModelRecordsResponse |

## List project models by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/`

Authentication requirements: `BearerAuth`

Lists all the models from a project.

.. minversion:: v2.34
    .. deprecated:: v2.34

```
    Use [GET /api/v2/projects/{projectId}/modelRecords/][get-apiv2projectsprojectidmodelrecords] instead.
    Fewer attributes are returned in the response of model records route.

    Removed attributes:

    monotonic_increasing_featurelist_id -- Retrievable from the blueprint level
    monotonic_decreasing_featurelist_id -- Retrievable from the blueprint level.
    supports_composable_ml -- Retrievable from the blueprint level.
    supports_monotonic_constraints -- Retrievable from the blueprint level.
    has_empty_clusters -- Retrievable from the individual model level.
    is_n_clusters_dynamically_determined -- Retrievable from the individual model level.
    prediction_threshold -- Retrievable from the individual model level.
    prediction_threshold_read_only - Retrievable from the individual model level.

    Changed attributes:
    n_clusters becomes number_of_clusters and is returned for unsupervised clustering models.
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. |
| showInSampleScores | query | boolean | false | If specified, will return metric scores for models trained into validation/holdout for projects that do not have stacked predictions. |
| name | query | string | false | If specified, filters for models with a model type matching name. |
| samplePct | query | number | false | If specified, filters for models with a matching sample percentage. |
| isStarred | query | string | false | If specified, filters for models marked as starred. |
| orderBy | query | string | false | A comma-separated list of metrics to sort by. If metric is prefixed with a '-', models are sorted by this metric in descending order, otherwise are sorted in ascending order. Valid sorting metrics are metric and samplePct. Use of metric sorts models by metric value selected for this project using the validation score. Use of the prefix accounts for the direction of the metric, so -metric will sort in order of decreasing 'goodness', which may be opposite to the natural numerical order. If not specified, -metric will be used. |
| projectId | path | string | true | The project ID. |

### Enumerated Values

| Parameter | Value |
| --- | --- |
| isStarred | [false, False, true, True] |
| orderBy | [metric, -metric, samplePct, -samplePct] |

### Example responses

> 200 Response

```
{
  "items": {
    "properties": {
      "blenderModels": {
        "description": "Models that are in the blender.",
        "items": {
          "type": "integer"
        },
        "maxItems": 100,
        "type": "array",
        "x-versionadded": "v2.36"
      },
      "blueprintId": {
        "description": "The blueprint used to construct the model.",
        "type": "string"
      },
      "dataSelectionMethod": {
        "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
        "enum": [
          "duration",
          "rowCount",
          "selectedDateRange",
          "useProjectSettings"
        ],
        "type": "string"
      },
      "externalPredictionModel": {
        "description": "If the model is an external prediction model.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "featurelistId": {
        "description": "The ID of the feature list used by the model.",
        "type": [
          "string",
          "null"
        ]
      },
      "featurelistName": {
        "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
        "type": [
          "string",
          "null"
        ]
      },
      "frozenPct": {
        "description": "The training percent used to train the frozen model.",
        "type": [
          "number",
          "null"
        ],
        "x-versionadded": "v2.36"
      },
      "hasCodegen": {
        "description": "If the model has a codegen JAR file.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "hasFinetuners": {
        "description": "Whether a model has fine tuners.",
        "type": "boolean"
      },
      "icons": {
        "description": "The icons associated with the model.",
        "type": [
          "integer",
          "null"
        ],
        "x-versionadded": "v2.36"
      },
      "id": {
        "description": "The ID of the model.",
        "type": "string"
      },
      "isAugmented": {
        "description": "Whether a model was trained using augmentation.",
        "type": "boolean"
      },
      "isBlender": {
        "description": "If the model is a blender.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "isCustom": {
        "description": "If the model contains custom tasks.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "isFrozen": {
        "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
        "type": "boolean"
      },
      "isNClustersDynamicallyDetermined": {
        "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
        "type": "boolean"
      },
      "isStarred": {
        "description": "Indicates whether the model has been starred.",
        "type": "boolean"
      },
      "isTrainedIntoHoldout": {
        "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
        "type": "boolean"
      },
      "isTrainedIntoValidation": {
        "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
        "type": "boolean"
      },
      "isTrainedOnGpu": {
        "description": "Whether the model was trained using GPU workers.",
        "type": "boolean",
        "x-versionadded": "v2.33"
      },
      "isTransparent": {
        "description": "If the model is a transparent model with exposed coefficients.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "isUserModel": {
        "description": "If the model was created with Composable ML.",
        "type": "boolean",
        "x-versionadded": "v2.36"
      },
      "lifecycle": {
        "description": "Object returning model lifecycle.",
        "properties": {
          "reason": {
            "description": "The reason for the lifecycle stage. None if the model is active.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.30"
          },
          "stage": {
            "description": "The model lifecycle stage.",
            "enum": [
              "active",
              "deprecated",
              "disabled"
            ],
            "type": "string",
            "x-versionadded": "v2.30"
          }
        },
        "required": [
          "reason",
          "stage"
        ],
        "type": "object"
      },
      "linkFunction": {
        "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
        "type": [
          "string",
          "null"
        ],
        "x-versionadded": "v2.21"
      },
      "metrics": {
        "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
        "type": "object"
      },
      "modelCategory": {
        "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
        "enum": [
          "model",
          "prime",
          "blend",
          "combined",
          "incrementalLearning"
        ],
        "type": "string"
      },
      "modelFamily": {
        "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
        "type": "string",
        "x-versionadded": "v2.21"
      },
      "modelFamilyFullName": {
        "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
        "type": "string",
        "x-versionadded": "v2.31"
      },
      "modelNumber": {
        "description": "The model number from the Leaderboard.",
        "exclusiveMinimum": 0,
        "type": [
          "integer",
          "null"
        ]
      },
      "modelType": {
        "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
        "type": "string"
      },
      "monotonicDecreasingFeaturelistId": {
        "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
        "type": [
          "string",
          "null"
        ],
        "x-versionadded": "v2.21"
      },
      "monotonicIncreasingFeaturelistId": {
        "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
        "type": [
          "string",
          "null"
        ],
        "x-versionadded": "v2.21"
      },
      "nClusters": {
        "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
        "type": [
          "integer",
          "null"
        ]
      },
      "parentModelId": {
        "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
        "type": [
          "string",
          "null"
        ]
      },
      "predictionThreshold": {
        "description": "threshold used for binary classification in predictions.",
        "maximum": 1,
        "minimum": 0,
        "type": "number",
        "x-versionadded": "v2.13"
      },
      "predictionThresholdReadOnly": {
        "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
        "type": "boolean",
        "x-versionadded": "v2.13"
      },
      "processes": {
        "description": "The list of processes used by the model.",
        "items": {
          "type": "string"
        },
        "maxItems": 100,
        "type": "array"
      },
      "projectId": {
        "description": "The ID of the project to which the model belongs.",
        "type": "string"
      },
      "samplePct": {
        "description": "The percentage of the dataset used in training the model.",
        "exclusiveMinimum": 0,
        "type": [
          "number",
          "null"
        ]
      },
      "samplingMethod": {
        "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
        "enum": [
          "random",
          "latest"
        ],
        "type": "string"
      },
      "supportsComposableMl": {
        "description": "indicates whether this model is supported in Composable ML.",
        "type": "boolean",
        "x-versionadded": "2.26"
      },
      "supportsMonotonicConstraints": {
        "description": "whether this model supports enforcing monotonic constraints",
        "type": "boolean",
        "x-versionadded": "v2.21"
      },
      "timeWindowSamplePct": {
        "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
        "exclusiveMaximum": 100,
        "exclusiveMinimum": 0,
        "type": [
          "integer",
          "null"
        ]
      },
      "trainingDuration": {
        "description": "the duration spanned by the dates in the partition column for the data used to train the model",
        "type": [
          "string",
          "null"
        ]
      },
      "trainingEndDate": {
        "description": "the end date of the dates in the partition column for the data used to train the model",
        "format": "date-time",
        "type": [
          "string",
          "null"
        ]
      },
      "trainingRowCount": {
        "description": "The number of rows used to train the model.",
        "exclusiveMinimum": 0,
        "type": [
          "integer",
          "null"
        ]
      },
      "trainingStartDate": {
        "description": "the start date of the dates in the partition column for the data used to train the model",
        "format": "date-time",
        "type": [
          "string",
          "null"
        ]
      }
    },
    "required": [
      "blenderModels",
      "blueprintId",
      "externalPredictionModel",
      "featurelistId",
      "featurelistName",
      "frozenPct",
      "hasCodegen",
      "icons",
      "id",
      "isBlender",
      "isCustom",
      "isFrozen",
      "isStarred",
      "isTrainedIntoHoldout",
      "isTrainedIntoValidation",
      "isTrainedOnGpu",
      "isTransparent",
      "isUserModel",
      "lifecycle",
      "linkFunction",
      "metrics",
      "modelCategory",
      "modelFamily",
      "modelFamilyFullName",
      "modelNumber",
      "modelType",
      "monotonicDecreasingFeaturelistId",
      "monotonicIncreasingFeaturelistId",
      "parentModelId",
      "predictionThreshold",
      "predictionThresholdReadOnly",
      "processes",
      "projectId",
      "samplePct",
      "supportsComposableMl",
      "supportsMonotonicConstraints",
      "trainingDuration",
      "trainingEndDate",
      "trainingRowCount",
      "trainingStartDate"
    ],
    "type": "object"
  },
  "type": "array"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The project's models. | Inline |

### Response Schema

Status Code 200

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| anonymous | [ModelDetailsResponse] | false |  | none |
| » blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| » blueprintId | string | true |  | The blueprint used to construct the model. |
| » dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| » externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| » featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| » featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| » frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| » hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| » hasFinetuners | boolean | false |  | Whether a model has fine tuners. |
| » icons | integer,null | true |  | The icons associated with the model. |
| » id | string | true |  | The ID of the model. |
| » isAugmented | boolean | false |  | Whether a model was trained using augmentation. |
| » isBlender | boolean | true |  | If the model is a blender. |
| » isCustom | boolean | true |  | If the model contains custom tasks. |
| » isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| » isNClustersDynamicallyDetermined | boolean | false |  | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. |
| » isStarred | boolean | true |  | Indicates whether the model has been starred. |
| » isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| » isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| » isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| » isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| » isUserModel | boolean | true |  | If the model was created with Composable ML. |
| » lifecycle | ModelLifecycle | true |  | Object returning model lifecycle. |
| »» reason | string,null | true |  | The reason for the lifecycle stage. None if the model is active. |
| »» stage | string | true |  | The model lifecycle stage. |
| » linkFunction | string,null | true |  | The link function the final modeler uses in the blueprint. If no link function exists, returns null. |
| » metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| » modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| » modelFamily | string | true |  | The family the model belongs to, e.g., SVM, GBM, etc. |
| » modelFamilyFullName | string | true |  | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. |
| » modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| » modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| » monotonicDecreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| » monotonicIncreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| » nClusters | integer,null | false |  | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| » parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| » predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. |
| » predictionThresholdReadOnly | boolean | true |  | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. |
| » processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| » projectId | string | true |  | The ID of the project to which the model belongs. |
| » samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| » samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| » supportsComposableMl | boolean | true |  | indicates whether this model is supported in Composable ML. |
| » supportsMonotonicConstraints | boolean | true |  | whether this model supports enforcing monotonic constraints |
| » timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| » trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| » trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| » trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| » trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| stage | [active, deprecated, disabled] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplingMethod | [random, latest] |

## Train a new model by project ID

Operation path: `POST /api/v2/projects/{projectId}/models/`

Authentication requirements: `BearerAuth`

Train a new model. To specify the amount of data to use to train the model, use either `samplePct` to express a percentage of the rows of the dataset to use or `trainingRowCount` to express the number of rows to use. If neither `samplePct` or `trainingRowCount` is specified, the model will be trained on the maximum available training data that can be used to train an in-memory model. For projects using smart sampling, samplePct and trainingRowCount will be interpreted as a percent or number of rows of the minority class. 
 When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use.

### Body parameter

```
{
  "properties": {
    "blueprintId": {
      "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.",
      "type": "string"
    },
    "featurelistId": {
      "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.",
      "type": [
        "string",
        "null"
      ]
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.",
      "type": [
        "string",
        "null"
      ]
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0 and 100.",
      "exclusiveMinimum": 0,
      "maximum": 100,
      "type": "number"
    },
    "scoringType": {
      "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.",
      "enum": [
        "validation",
        "crossValidation"
      ],
      "type": "string"
    },
    "sourceProjectId": {
      "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "An integer representing the number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.",
      "type": "integer"
    }
  },
  "required": [
    "blueprintId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | TrainModel | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Creation has successfully started. See the Location header. | None |
| 422 | Unprocessable Entity | Could not create new job. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Retrain a model by project ID

Operation path: `POST /api/v2/projects/{projectId}/models/fromModel/`

Authentication requirements: `BearerAuth`

Retrain an existing model using a new sample size and/or feature list.When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use.
Note that only one of `samplePct` or `trainingRowCount` should be specified.

### Body parameter

```
{
  "properties": {
    "featurelistId": {
      "description": "If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before.",
      "type": "string"
    },
    "modelId": {
      "description": "The model to be retrained.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used.",
      "exclusiveMinimum": 0,
      "maximum": 100,
      "type": "number"
    },
    "scoringType": {
      "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.",
      "enum": [
        "validation",
        "crossValidation"
      ],
      "type": "string",
      "x-versionadded": "v2.23"
    },
    "trainingRowCount": {
      "description": "The number of rows to use to train the model. If not specified, original model training row count will be used.",
      "exclusiveMinimum": 0,
      "type": "integer"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | RetrainModel | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Retrain an existing model using a new sample size and/or feature list. | ModelRetrainResponse |
| 422 | Unprocessable Entity | model with specified modelId is deprecated, or it doesn't support retraining using a new sample size and/or feature list | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Delete a model by project ID

Operation path: `DELETE /api/v2/projects/{projectId}/models/{modelId}/`

Authentication requirements: `BearerAuth`

Delete a model from the leaderboard.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 204 | No Content | The model has been successfully deleted. | None |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | Unable to process the request. | None |

## Get model by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/`

Authentication requirements: `BearerAuth`

Look up a particular model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The model. | ModelDetailsResponse |

## Update a model's attributes by project ID

Operation path: `PATCH /api/v2/projects/{projectId}/models/{modelId}/`

Authentication requirements: `BearerAuth`

Update a model's attributes.

### Body parameter

```
{
  "properties": {
    "isStarred": {
      "description": "Mark model either as starred or unstarred.",
      "type": "boolean"
    },
    "predictionThreshold": {
      "description": "Threshold used for binary classification in predictions. Default value is 0.5.",
      "maximum": 1,
      "minimum": 0,
      "type": "number"
    }
  },
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |
| body | body | ModelUpdate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 204 | No Content | The model has been successfully updated with new attributes. | None |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | Unable to process the request. | None |

## Create advanced tuning by id

Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/`

Authentication requirements: `BearerAuth`

Submit a job to make a new version of the model with different advanced tuning parameters. Note: This route currently supports all models other than: OSS, blenders, prime, scaleout, baseline and user-created. Currently, only single-stage models (most simple models) are supported. Blueprints that run multiple steps, for example one step to predict zero vs nonzero and a second step to determine the value of nonzero predictions, are not supported. (:ref: `Advanced Tuning documentation <grid_search>`). Parameters may be omitted from this endpoint. If a parameter is omitted, its `currentValue` will be used. To see the possible parameter IDs and constraints on possible values, see [GET /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/parameters/][get-apiv2projectsprojectidmodelsmodelidadvancedtuningparameters].

### Body parameter

```
{
  "properties": {
    "gridSearchArguments": {
      "description": "The details of the grid search configuration.",
      "properties": {
        "algorithm": {
          "default": "Pattern Search",
          "description": "The algorithm to apply when running the grid search.",
          "enum": [
            "Pattern Search",
            "Accelerated Search",
            "Exhaustive Search",
            "Greedy Exhaustive Search",
            "Bayesian TPE Search",
            "Bayesian GP Search"
          ],
          "type": "string"
        },
        "batchSize": {
          "default": 2,
          "description": "The number of iterations to perform in each batch.",
          "maximum": 8,
          "minimum": 1,
          "type": "integer"
        },
        "maxIterations": {
          "default": 100,
          "description": "Sets the maximum number of iterations to perform.",
          "maximum": 10000,
          "minimum": 1,
          "type": "integer"
        },
        "randomState": {
          "default": 42,
          "description": "The random state/seed used for the grid search.",
          "minimum": 0,
          "type": "integer"
        },
        "searchType": {
          "default": "smart",
          "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.",
          "enum": [
            "full",
            "smart",
            "bayesian"
          ],
          "type": "string"
        },
        "wallClockTimeLimit": {
          "default": 300,
          "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.",
          "maximum": 604800,
          "minimum": 1,
          "type": "integer"
        }
      },
      "type": "object",
      "x-versionadded": "v2.38"
    },
    "tuningDescription": {
      "description": "Human-readable description of this advanced-tuning request.",
      "type": "string"
    },
    "tuningParameters": {
      "description": "Parameters to tune.",
      "items": {
        "properties": {
          "parameterId": {
            "description": "ID of the parameter whose value to set.",
            "type": "string"
          },
          "value": {
            "description": "Value for the specified parameter.",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          }
        },
        "required": [
          "parameterId",
          "value"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "tuningParameters"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |
| body | body | ModelAdvancedTuning | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The job has been successfully submitted. See the Location header. | None |
| 403 | Forbidden | Permission denied creating advanced tuned model | None |
| 404 | Not Found | This resource does not exist. | None |
| 413 | Payload Too Large | Tuning request is too large | None |
| 422 | Unprocessable Entity | Could not create new job. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | A url at which the job processing the model can be retrieved. |

## Retrieve information about all advanced tuning parameters available by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/parameters/`

Authentication requirements: `BearerAuth`

Retrieve information about all advanced tuning parameters available for the specified model. Note: This route currently supports all models other than: OSS, blenders, prime, scaleout, baseline and user-created.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "tuningDescription": {
      "description": "Human-readable description of the tuned model, if specified by the user. `null` if unspecified.",
      "type": [
        "string",
        "null"
      ]
    },
    "tuningParameters": {
      "description": "An array of objects containing information about tuning parameters that are supported by the specified model.",
      "items": {
        "properties": {
          "constraints": {
            "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.",
            "properties": {
              "ascii": {
                "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "float": {
                "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).",
                "properties": {
                  "max": {
                    "description": "Maximum value for the parameter.",
                    "type": "number"
                  },
                  "min": {
                    "description": "Minimum value for the parameter.",
                    "type": "number"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "max",
                  "min",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "floatList": {
                "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).",
                "properties": {
                  "maxLength": {
                    "description": "Maximum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "maxVal": {
                    "description": "Maximum permitted value.",
                    "type": "number"
                  },
                  "minLength": {
                    "description": "Minimum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "minVal": {
                    "description": "Minimum permitted value.",
                    "type": "number"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "maxLength",
                  "maxVal",
                  "minLength",
                  "minVal",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "int": {
                "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.",
                "properties": {
                  "max": {
                    "description": "Maximum value for the parameter.",
                    "type": "integer"
                  },
                  "min": {
                    "description": "Minimum value for the parameter.",
                    "type": "integer"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "max",
                  "min",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "intList": {
                "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.",
                "properties": {
                  "maxLength": {
                    "description": "Maximum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "maxVal": {
                    "description": "Maximum permitted value.",
                    "type": "integer"
                  },
                  "minLength": {
                    "description": "Minimum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "minVal": {
                    "description": "Minimum permitted value.",
                    "type": "integer"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "maxLength",
                  "maxVal",
                  "minLength",
                  "minVal",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "select": {
                "description": "Indicates that the value can be one selected from a list of known values.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  },
                  "values": {
                    "description": "List of valid values for this field.",
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                },
                "required": [
                  "supportsGridSearch",
                  "values"
                ],
                "type": "object"
              },
              "selectgrid": {
                "description": "Indicates that the value can be one selected from a list of known values.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  },
                  "values": {
                    "description": "List of valid values for this field.",
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                },
                "required": [
                  "supportsGridSearch",
                  "values"
                ],
                "type": "object"
              },
              "unicode": {
                "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "supportsGridSearch"
                ],
                "type": "object"
              }
            },
            "type": "object"
          },
          "currentValue": {
            "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          },
          "defaultValue": {
            "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          },
          "parameterId": {
            "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.",
            "type": "string"
          },
          "parameterName": {
            "description": "Name of the parameter.",
            "type": "string"
          },
          "taskName": {
            "description": "Human-readable name of the task that this parameter belongs to.",
            "type": "string"
          },
          "vertexId": {
            "description": "Id of the vertex this parameter belongs to.",
            "type": "string",
            "x-versionadded": "v2.29"
          }
        },
        "required": [
          "constraints",
          "currentValue",
          "defaultValue",
          "parameterId",
          "parameterName",
          "taskName",
          "vertexId"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "tuningDescription",
    "tuningParameters"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The information about all advanced tuning parameters available for the specified model. | AdvancedTuningArgumentsRetrieveResponse |
| 403 | Forbidden | Permission denied creating advanced tuned model. | None |
| 404 | Not Found | This resource does not exist. | None |
| 422 | Unprocessable Entity | This model does not support advanced tuning. | None |

## Retrieve cluster names assigned by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/clusterNames/`

Authentication requirements: `BearerAuth`

Retrieve all cluster names assigned to an unsupervised cluster model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "clusters": {
      "description": "A list of the model's cluster information entries.",
      "items": {
        "properties": {
          "name": {
            "description": "A cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          },
          "percent": {
            "description": "The percentage of rows in the dataset this cluster contains.",
            "maximum": 100,
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "name"
        ],
        "type": "object"
      },
      "maxItems": 100,
      "minItems": 2,
      "type": "array"
    },
    "modelId": {
      "description": "The model ID",
      "type": "string"
    },
    "projectId": {
      "description": "The project ID",
      "type": "string"
    }
  },
  "required": [
    "clusters",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Retrieve all cluster names for the model | ClusterNamesResponse |
| 404 | Not Found | Could not find unsupervised clustering model. Possible reasons include: 1. Provided model id points to a model that does not exist in specified project. 2. Provided model has incompatible type. Method requires model to be unsupervised clustering model. | None |

## Update cluster names assigned by project ID

Operation path: `PATCH /api/v2/projects/{projectId}/models/{modelId}/clusterNames/`

Authentication requirements: `BearerAuth`

Update and then retrieve all cluster names assigned to an unsupervised cluster model.

### Body parameter

```
{
  "properties": {
    "clusterNameMappings": {
      "description": "\n            A list of the mappings from a cluster's current name to its new name.\n            After update, value passed as a new name will become cluster's current name.\n            All cluster names should be unique and should identify one and only one cluster.\n            ",
      "items": {
        "properties": {
          "currentName": {
            "description": "Current cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          },
          "newName": {
            "description": "New cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "currentName",
          "newName"
        ],
        "type": "object"
      },
      "maxItems": 100,
      "type": "array"
    }
  },
  "required": [
    "clusterNameMappings"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |
| body | body | ClusterNamesUpdateParam | false | none |

### Example responses

> 200 Response

```
{
  "properties": {
    "clusters": {
      "description": "A list of the model's cluster information entries.",
      "items": {
        "properties": {
          "name": {
            "description": "A cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          },
          "percent": {
            "description": "The percentage of rows in the dataset this cluster contains.",
            "maximum": 100,
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "name"
        ],
        "type": "object"
      },
      "maxItems": 100,
      "minItems": 2,
      "type": "array"
    },
    "modelId": {
      "description": "The model ID",
      "type": "string"
    },
    "projectId": {
      "description": "The project ID",
      "type": "string"
    }
  },
  "required": [
    "clusters",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Update cluster names and then retrieve all cluster names for the model | ClusterNamesResponse |
| 404 | Not Found | Could not find unsupervised clustering model. Possible reasons include: 1. Provided model id points to a model which does not exists in specified project. 2. Provided model has incompatible type. Method requires model to be unsupervised clustering model. | None |
| 422 | Unprocessable Entity | The request cannot be processed. Possible reasons include: 1. Mapping contains invalid current cluster name and referenced cluster was not found. 2. Mapping is invalid as after update, clusters will not be uniquely identifiable by name. | None |

## Run cross validation by project ID

Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/crossValidation/`

Authentication requirements: `BearerAuth`

Run cross validation on a model.

### Body parameter

```
{
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |
| body | body | Empty | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The model has been successfully submitted. | None |
| 422 | Unprocessable Entity | Unable to process the request. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | Contains a url at which the job processing the model can be retrieved |

## Get cross validation scores by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/crossValidationScores/`

Authentication requirements: `BearerAuth`

Get Cross Validation scores for each partition in a model.
        .. note:: Individual partition scores are only available for newer models; older models that
                  have cross validation score calculated will need to be retrained.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| metric | query | string | false | Set to the name of a metric to only return results for that metric. |
| partition | query | number | false | Set to a value such as 1.0, 2.0 to only return results for the specified partition. |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "cvScores": {
      "description": "A dictionary `cvScores` with sub-dictionary keyed by `partition_id`, each `partition_id` is itself a dictionary keyed by `metric_name` where the value is the reading for that particular metric for the partition_id.",
      "example": "\n        {\n            \"cvScores\": {\n                \"FVE Gamma\": {\n                    \"0.0\": 0.24334,\n                    \"1.0\": 0.17757,\n                    \"2.0\": 0.21803,\n                    \"3.0\": 0.20185,\n                    \"4.0\": 0.20576\n                },\n                \"FVE Poisson\": {\n                    \"0.0\": 0.24527,\n                    \"1.0\": 0.22092,\n                    \"2.0\": 0.22451,\n                    \"3.0\": 0.24417,\n                    \"4.0\": 0.21654\n                }\n            }\n        }\n",
      "type": "object"
    }
  },
  "required": [
    "cvScores"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The Cross Validation scores for each partition in a model. | CrossValidationRetrieveResponse |
| 404 | Not Found | Not found. | None |

## List the features used by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/features/`

Authentication requirements: `BearerAuth`

List the features used in a model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "aPrioriFeatureNames": {
      "description": "(Deprecated in version v2.11) Renamed to `knownInAdvanceFeatureNames`. This parameter always has the same value as `knownInAdvanceFeatureNames` and will be removed in a future release.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "featureNames": {
      "description": "An array of the names of all features used by the specified model.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "knownInAdvanceFeatureNames": {
      "description": "An array of the names of time series known-in-advance features used by the specified model.",
      "items": {
        "type": "string"
      },
      "type": "array"
    }
  },
  "required": [
    "aPrioriFeatureNames",
    "featureNames",
    "knownInAdvanceFeatureNames"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The features used in a model. | ModelFeatureListResponse |
| 404 | Not Found | This resource does not exist. | None |

## Retrieve grid search scores by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/gridSearchScores/`

Authentication requirements: `BearerAuth`

Retrieve grid search scores.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
| source | query | string | false | The source for the scores. If omitted, the validation partition is used. |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Enumerated Values

| Parameter | Value |
| --- | --- |
| source | validation |

### Example responses

> 200 Response

```
{
  "properties": {
    "id": {
      "description": "The grid search scores metadata ID.",
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the model that scores belong to.",
      "type": "string"
    },
    "parameters": {
      "description": "The list of grid search parameters.",
      "items": {
        "items": {
          "properties": {
            "parameterName": {
              "description": "The name of the parameter.",
              "type": "string"
            },
            "parameterValue": {
              "description": "The value of the parameter.",
              "type": "number"
            }
          },
          "required": [
            "parameterName",
            "parameterValue"
          ],
          "type": "object",
          "x-versionadded": "v2.39"
        },
        "type": "array"
      },
      "maxItems": 100000,
      "minItems": 1,
      "type": "array"
    },
    "projectId": {
      "description": "The project the model belongs to.",
      "type": "string"
    },
    "scores": {
      "description": "The list of grid search scores.",
      "items": {
        "type": "number"
      },
      "maxItems": 100000,
      "minItems": 1,
      "type": "array"
    },
    "source": {
      "description": "The source for the scores. If omitted, the validation partition is used.",
      "enum": [
        "validation"
      ],
      "type": "string"
    }
  },
  "required": [
    "id",
    "modelId",
    "parameters",
    "projectId",
    "scores",
    "source"
  ],
  "type": "object",
  "x-versionadded": "v2.39"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The list of grid search scores. | GridSearchScoresRetrieveResponse |
| 422 | Unprocessable Entity | Unable to process the request. | None |

## Retrieve a summary of how the model's subtasks handle missing values by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/missingReport/`

Authentication requirements: `BearerAuth`

Retrieve a summary of how the model's subtasks handle missing values
        Only models built after the missing value report feature was added will have reports,
        and only models with at least one imputation or encoding task, e.g. ordinal encoding,
        missing value imputation. Blenders and scaleout models do not support Missing Value reports.

```
    The report will describe how each feature's missing values were treated, and report how many
    missing values were present in the training data. Features which were not processed by a
    given blueprint task will not mention it: for instance, a categorical feature with many
    unique values may not be considered eligible for processing by a One-Hot Encoding

    Report is collected for those features which are considered eligible by given
    blueprint task. For instance, categorical feature with a lot of unique values may not be
    considered as eligible in One-Hot Encoding Task.
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "missingValuesReport": {
      "description": "Missing values report, which contains an array of reports for individual features.",
      "items": {
        "properties": {
          "feature": {
            "description": "The name of the feature.",
            "type": "string"
          },
          "missingCount": {
            "description": "The number of missing values in the training data.",
            "type": "integer"
          },
          "missingPercentage": {
            "description": "The percentage of missing values in the training data.",
            "maximum": 1,
            "minimum": 0,
            "type": "number"
          },
          "tasks": {
            "additionalProperties": {
              "properties": {
                "descriptions": {
                  "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.",
                  "items": {
                    "type": "string"
                  },
                  "type": "array"
                },
                "name": {
                  "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.",
                  "type": "string"
                }
              },
              "required": [
                "descriptions",
                "name"
              ],
              "type": "object"
            },
            "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.",
            "type": "object"
          },
          "type": {
            "description": "The type of the feature, e.g., `Categorical` or `Numeric`.",
            "type": "string"
          }
        },
        "required": [
          "feature",
          "missingCount",
          "missingPercentage",
          "tasks",
          "type"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "missingValuesReport"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Retrieve a summary of how the model's subtasks handle missing values. | MissingReportRetrieve |
| 404 | Not Found | Could not find missing value report for provided project and model ID. | None |

## Get number of iterations trained by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/numIterationsTrained/`

Authentication requirements: `BearerAuth`

Retrieve the actual number of iterations or estimators trained by a tree-based early stopping model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "data": {
      "description": "Number of estimators or iterations for a single model stage",
      "items": {
        "properties": {
          "numIterations": {
            "description": "The number of iterations run in this stage of modeling.",
            "minimum": 0,
            "type": "integer"
          },
          "stage": {
            "description": "Modeling stage or None if it is a single-stage model",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "numIterations",
          "stage"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "modelId": {
      "description": "The model ID.",
      "type": "string"
    },
    "projectId": {
      "description": "The project ID.",
      "type": "string"
    }
  },
  "required": [
    "data",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The number of estimators/iterations trained | NumIterationsTrainedResponse |
| 404 | Not Found | Cannot retrieve early stopping information for this model. | None |

## Retrieve model parameters by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/parameters/`

Authentication requirements: `BearerAuth`

Retrieve model parameters. These are the parameters that appear in the webapp on the `Coefficients` tab. Note that they are only available for some models.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "derivedFeatures": {
      "description": "An array of preprocessing information about derived features used in the model.",
      "items": {
        "properties": {
          "coefficient": {
            "description": "The coefficient for this feature.",
            "type": "number"
          },
          "derivedFeature": {
            "description": "The name of the derived feature.",
            "type": "string"
          },
          "originalFeature": {
            "description": "The name of the feature used to derive this feature.",
            "type": "string"
          },
          "stageCoefficients": {
            "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).",
            "items": {
              "properties": {
                "coefficient": {
                  "description": "The corresponding value of the coefficient for that stage.",
                  "type": "number"
                },
                "stage": {
                  "description": "The name of the stage.",
                  "type": "string"
                }
              },
              "required": [
                "coefficient",
                "stage"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "transformations": {
            "description": "An array of json objects describing the transformations applied to create this derived feature.",
            "items": {
              "properties": {
                "name": {
                  "description": "The name of the transformation.",
                  "type": "string"
                },
                "value": {
                  "description": "The value used in carrying it out.",
                  "type": "string"
                }
              },
              "required": [
                "name",
                "value"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "type": {
            "description": "The type of this feature.",
            "type": "string"
          }
        },
        "required": [
          "coefficient",
          "derivedFeature",
          "originalFeature",
          "stageCoefficients",
          "transformations",
          "type"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "parameters": {
      "description": "An array of parameters that are related to the whole model.",
      "items": {
        "properties": {
          "name": {
            "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".",
            "type": "string"
          },
          "value": {
            "description": "The value of the parameter.",
            "type": "string"
          }
        },
        "required": [
          "name",
          "value"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "derivedFeatures",
    "parameters"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The parameters of the model. | ModelParametersRetrieveResponse |
| 422 | Unprocessable Entity | Unable to process the request. | None |

## Retrieve prediction intervals that are already calculated by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/predictionIntervals/`

Authentication requirements: `BearerAuth`

Retrieve prediction intervals (in descending order) that are already calculated for this model.
Note that the project this model belongs to must be a time series project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
| projectId | path | string | true | The project to retrieve prediction intervals for. Must be a time series project. |
| modelId | path | string | true | The model to retrieve prediction intervals for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "A descending-ordered array of already-calculated prediction intervals percentiles.",
      "items": {
        "exclusiveMinimum": 0,
        "maximum": 100,
        "type": "integer"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Request was successful. | PredictionIntervalsListResponse |

## Calculate prediction intervals by project ID

Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/predictionIntervals/`

Authentication requirements: `BearerAuth`

Submit a job to calculate prediction intervals for the specified percentiles for this model. 
Note that the project this model belongs to must be a time series project.

### Body parameter

```
{
  "properties": {
    "percentiles": {
      "description": "The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time.",
      "items": {
        "exclusiveMinimum": 0,
        "maximum": 100,
        "type": "integer"
      },
      "maxItems": 1,
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "percentiles"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project to calculate prediction intervals for. Must be a time series project. |
| modelId | path | string | true | The model to calculate prediction intervals for. |
| body | body | PredictionIntervalsCreate | false | none |

### Example responses

> 202 Response

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Job was successfully submitted. See Location header. | PredictionIntervalsCreateResponse |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string |  | A url that can be polled to check the status. |

## Check a Model for Prime Eligibility by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/`

Authentication requirements: `BearerAuth`

Check if a model can be approximated by DataRobot Prime.  Deprecated in v2.35.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the model belongs to. |
| modelId | path | string | true | The model to check. |

### Example responses

> 200 Response

```
{
  "properties": {
    "canMakePrime": {
      "description": "Indicates whether the requested model is a valid input for creating a Prime model.",
      "type": "boolean"
    },
    "message": {
      "description": "May contain details about why a model is not eligible for DataRobot Prime.",
      "type": "string"
    },
    "messageId": {
      "description": "An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models.",
      "type": "integer"
    }
  },
  "required": [
    "canMakePrime",
    "message",
    "messageId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | PrimeInfoRetrieveResponse |

## List rulesets by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/primeRulesets/`

Authentication requirements: `BearerAuth`

List all the rulesets approximating a model

When rulesets are created for the parent model, all of the rulesets are created at once, but not all rulesets have corresponding Prime models (until they are directly requested).

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the model belongs to. |
| modelId | path | string | true | The model to find approximating rulesets for. |

### Example responses

> 200 Response

```
{
  "items": {
    "properties": {
      "modelId": {
        "description": "The ID of the Prime model using this ruleset (if it exists) or null.",
        "type": "string"
      },
      "parentModelId": {
        "description": "The ID of the model this ruleset approximates.",
        "type": "string"
      },
      "projectId": {
        "description": "The project this ruleset belongs to.",
        "type": "string"
      },
      "ruleCount": {
        "description": "The number of rules used by this ruleset.",
        "type": "integer"
      },
      "rulesetId": {
        "description": "The ID of the ruleset.",
        "type": "integer"
      },
      "score": {
        "description": "The validation score of the ruleset.",
        "type": "number"
      }
    },
    "required": [
      "modelId",
      "parentModelId",
      "projectId",
      "ruleCount",
      "rulesetId",
      "score"
    ],
    "type": "object"
  },
  "type": "array"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | Inline |

### Response Schema

Status Code 200

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| anonymous | [PrimeRulesetsListResponse] | false |  | none |
| » modelId | string | true |  | The ID of the Prime model using this ruleset (if it exists) or null. |
| » parentModelId | string | true |  | The ID of the model this ruleset approximates. |
| » projectId | string | true |  | The project this ruleset belongs to. |
| » ruleCount | integer | true |  | The number of rules used by this ruleset. |
| » rulesetId | integer | true |  | The ID of the ruleset. |
| » score | number | true |  | The validation score of the ruleset. |

## Create Rulesets by project ID

Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/primeRulesets/`

Authentication requirements: `BearerAuth`

Approximate an existing model on the leaderboard with DataRobot Prime. A request body should be an empty JSON {}. Deprecated in v2.35.

### Body parameter

```
{
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the model to approximate belongs to. |
| modelId | path | string | true | The model to approximate. |
| body | body | PrimeRulesetsCreatePayload | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The request was understood and accepted, and is now being worked on. See the Location header. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | A URL that can be polled to check the status of the job. |

## Retrieve scoring code by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/scoringCode/`

Authentication requirements: `BearerAuth`

Retrieve Scoring Code for making new predictions from an existing model offline.
You need the "Scoring Code" feature enabled to use this route.

By default, returns a compiled executable JAR that can be executed locally to calculate model predictions, or it can be used as a library for a Java application. Execute it with the '--help` parameters to learn how to use it as a command-line utility.
See model API documentation (https://javadoc.io/doc/com.datarobot/datarobot-prediction/latest/index.html) to be able to use it inside an existing Java application.

With the sourceCode query parameter set to 'true', returns a source code archive that can be used to review internal calculations of the model. This JAR is NOT executable.

See "https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/index.html" in DataRobot application for more information.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| sourceCode | query | string | false | If set to "true", the downloaded JAR file will contain only the source code and will not be executable. |
| projectId | path | string | true | The project that created the model. |
| modelId | path | string | true | The model to use. |

### Enumerated Values

| Parameter | Value |
| --- | --- |
| sourceCode | [false, False, true, True] |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The JAR file. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Content-Disposition | string |  | attachment; filename="<"filename">".jar The suggested filename for the scoring code is dynamically generated |
| 200 | Content-Type | string |  | application/java-archive |

## Get supported capabilities by project ID

Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/supportedCapabilities/`

Authentication requirements: `BearerAuth`

Get supported capabilities for a model.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| modelId | path | string | true | The model ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "eligibleForPrime": {
      "description": "`True` if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible.",
      "type": "boolean",
      "x-versiondeprecated": "v2.35"
    },
    "hasParameters": {
      "description": "`True` if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters.",
      "type": "boolean"
    },
    "hasWordCloud": {
      "description": "True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud.",
      "type": "boolean"
    },
    "reasons": {
      "description": "Information on why the capability is unsupported for the model.",
      "properties": {
        "supportsAccuracyOverTime": {
          "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsAnomalyAssessment": {
          "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsAnomalyOverTime": {
          "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsClusterInsights": {
          "description": "If present, the reason why Cluster Insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsConfusionMatrix": {
          "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.33"
        },
        "supportsDocumentTextExtractionSampleInsight": {
          "description": "If present, the reason document text extraction sample insights are not supported for the model.",
          "type": "string",
          "x-versionadded": "v2.29"
        },
        "supportsForecastAccuracy": {
          "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsForecastVsActual": {
          "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsImageActivationMaps": {
          "description": "If present, the reason image activation maps are not supported for the model.",
          "type": "string"
        },
        "supportsImageEmbedding": {
          "description": "If present, the reason image embeddings are not supported for the model.",
          "type": "string"
        },
        "supportsLiftChart": {
          "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.31"
        },
        "supportsPeriodAccuracy": {
          "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsPredictionExplanations": {
          "description": "If present, the reason why Prediction Explanations cannot be computed for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsPredictionIntervals": {
          "description": "If present, the reason why Prediction Intervals cannot be computed for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsResiduals": {
          "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.30"
        },
        "supportsRocCurve": {
          "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.32"
        },
        "supportsSeriesInsights": {
          "description": "If present, the reason why Series Insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsStability": {
          "description": "If present, the reason why Stability plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        }
      },
      "type": "object"
    },
    "supportsAccuracyOverTime": {
      "description": "`True` if Accuracy Over Time plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsAdvancedTuning": {
      "description": "`True` if model supports Advanced Tuning.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "supportsAnomalyAssessment": {
      "description": "`True` if Anomaly Assessment insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsAnomalyOverTime": {
      "description": "`True` if Anomaly Over Time plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsBlending": {
      "description": "`True` if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations.",
      "type": "boolean"
    },
    "supportsClusterInsights": {
      "description": "`True` if Cluster Insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsCodeGeneration": {
      "description": "`True` if the model supports export of model's source code or compiled Java executable.",
      "type": "boolean",
      "x-versionadded": "v2.18"
    },
    "supportsCoefficients": {
      "description": "`True` if model coefficients are available.",
      "type": "boolean",
      "x-versionadded": "v2.32"
    },
    "supportsConfusionMatrix": {
      "description": "`True` if Confusion Matrix can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "supportsDocumentTextExtractionSampleInsight": {
      "description": "`True` if the model has document column(s) and document text extraction samples can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "supportsEarlyStopping": {
      "description": "`True` if this is an early stopping tree-based model and number of trained iterations can be retrieved.",
      "type": "boolean",
      "x-versionadded": "v2.22"
    },
    "supportsForecastAccuracy": {
      "description": "`True` if Forecast Accuracy plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsForecastVsActual": {
      "description": "`True` if Forecast vs Actual plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsImageActivationMaps": {
      "description": "`True` if the model has image column(s) and activation maps can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsImageEmbedding": {
      "description": "`True` if the model has image column(s) and image embeddings can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsLiftChart": {
      "description": "`True` if Lift Chart can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.31"
    },
    "supportsModelPackageExport": {
      "description": "`True` if the model can be exported as a model package.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsModelTrainingMetrics": {
      "description": "When `True` , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion.",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "supportsMonotonicConstraints": {
      "description": "`True` if the model supports monotonic constraints.",
      "type": "boolean"
    },
    "supportsNNVisualizations": {
      "description": "`True` if the model supports neuralNetworkVisualizations.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsPerLabelMetrics": {
      "description": "`True` if the experiment qualifies as a multilabel project.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "supportsPeriodAccuracy": {
      "description": "`True` if Period Accuracy insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionExplanations": {
      "description": "`True` if the model supports Prediction Explanations.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionIntervals": {
      "description": "`True` if Prediction Intervals can be computed for predictions generated by this model.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsResiduals": {
      "description": "When `True`, the model supports residuals and residuals data can be retrieved.",
      "type": "boolean",
      "x-versionadded": "v2.30"
    },
    "supportsRocCurve": {
      "description": "`True` if ROC Curve can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.32"
    },
    "supportsSeriesInsights": {
      "description": "`True` if Series Insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsShap": {
      "description": "`True` if the model supports Shapley package, i.e., Shapley-based feature importance.",
      "type": "boolean",
      "x-versionadded": "v2.18"
    },
    "supportsStability": {
      "description": "`True` if Stability plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    }
  },
  "required": [
    "eligibleForPrime",
    "hasParameters",
    "hasWordCloud",
    "supportsAccuracyOverTime",
    "supportsAdvancedTuning",
    "supportsAnomalyAssessment",
    "supportsAnomalyOverTime",
    "supportsBlending",
    "supportsClusterInsights",
    "supportsCodeGeneration",
    "supportsCoefficients",
    "supportsConfusionMatrix",
    "supportsDocumentTextExtractionSampleInsight",
    "supportsForecastAccuracy",
    "supportsForecastVsActual",
    "supportsImageActivationMaps",
    "supportsImageEmbedding",
    "supportsLiftChart",
    "supportsModelTrainingMetrics",
    "supportsMonotonicConstraints",
    "supportsNNVisualizations",
    "supportsPerLabelMetrics",
    "supportsPredictionExplanations",
    "supportsPredictionIntervals",
    "supportsResiduals",
    "supportsRocCurve",
    "supportsSeriesInsights",
    "supportsShap",
    "supportsStability"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Successfully returned model capability information. | ModelCapabilitiesRetrieveResponse |
| 404 | Not Found | Resource not found. | None |

## Get Prime files by project ID

Operation path: `GET /api/v2/projects/{projectId}/primeFiles/`

Authentication requirements: `BearerAuth`

List all DataRobot Prime files available for download.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | The number of results to skip. |
| limit | query | integer | false | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. |
| parentModelId | query | string | false | If specified, only Prime files approximating the specified parent model will be returned; otherwise all applicable Prime files will be returned. |
| modelId | query | string | false | If specified, only Prime files with code used in the specified prime model will be returned; otherwise all applicable Prime files will be returned. |
| projectId | path | string | true | The project to list available files for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.",
      "items": {
        "properties": {
          "id": {
            "description": "The ID of the file.",
            "type": "string"
          },
          "isValid": {
            "description": "Whether the code passed basic validation checks.",
            "type": "boolean"
          },
          "language": {
            "description": "The language the code is written in (e.g., Python).",
            "enum": [
              "Python",
              "Java"
            ],
            "type": "string"
          },
          "modelId": {
            "description": "The ID of the Prime model.",
            "type": "string"
          },
          "parentModelId": {
            "description": "The ID of the model this code approximates.",
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project the file belongs to.",
            "type": "string"
          },
          "rulesetId": {
            "description": "The ID of the ruleset this code uses to approximate the parent model.",
            "type": "integer"
          }
        },
        "required": [
          "id",
          "isValid",
          "language",
          "modelId",
          "parentModelId",
          "projectId",
          "rulesetId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | PrimeFileListResponse |

## Create a Prime File by project ID

Operation path: `POST /api/v2/projects/{projectId}/primeFiles/`

Authentication requirements: `BearerAuth`

Request creation and validation of source code from a Prime model. Deprecated in v2.35.

### Body parameter

```
{
  "properties": {
    "language": {
      "description": "The desired language of the generated code.",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The Prime model to generate code for.",
      "type": "string"
    }
  },
  "required": [
    "language",
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the Prime model belongs to. |
| body | body | PrimeFileCreate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The prime validation job was added to queue. See the Location header. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | A URL that can be polled to check the status of the prime validation job. |

## Retrieve metadata about a DataRobot Prime file by project ID

Operation path: `GET /api/v2/projects/{projectId}/primeFiles/{primeFileId}/`

Authentication requirements: `BearerAuth`

Retrieve metadata about a DataRobot Prime file available for download.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the file belongs to. |
| primeFileId | path | string | true | The file to retrieve. |

### Example responses

> 200 Response

```
{
  "properties": {
    "id": {
      "description": "The ID of the file.",
      "type": "string"
    },
    "isValid": {
      "description": "Whether the code passed basic validation checks.",
      "type": "boolean"
    },
    "language": {
      "description": "The language the code is written in (e.g., Python).",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the Prime model.",
      "type": "string"
    },
    "parentModelId": {
      "description": "The ID of the model this code approximates.",
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project the file belongs to.",
      "type": "string"
    },
    "rulesetId": {
      "description": "The ID of the ruleset this code uses to approximate the parent model.",
      "type": "integer"
    }
  },
  "required": [
    "id",
    "isValid",
    "language",
    "modelId",
    "parentModelId",
    "projectId",
    "rulesetId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | PrimeFileResponse |

## Download code by project ID

Operation path: `GET /api/v2/projects/{projectId}/primeFiles/{primeFileId}/download/`

Authentication requirements: `BearerAuth`

Download code from an existing Prime file.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the file belongs to. |
| primeFileId | path | string | true | The Prime file to download code from. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The response will contain a file with the executable code from the Prime file. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Content-Disposition | string |  | Will be attachment; filename="". The suggested filename will depend on the language the Prime file was generated for. |

## List all Prime models by project ID

Operation path: `GET /api/v2/projects/{projectId}/primeModels/`

Authentication requirements: `BearerAuth`

List all Prime models in a project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| offset | query | integer | false | The number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| projectId | path | string | true | The project to list models from. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.",
      "items": {
        "properties": {
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "ruleCount": {
            "description": "The number of rules used to create this model.",
            "type": "integer"
          },
          "rulesetId": {
            "description": "The ID of the ruleset this model uses.",
            "type": "integer"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "score": {
            "description": "The validation score of the model's ruleset.",
            "type": "number"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "ruleCount",
          "rulesetId",
          "samplePct",
          "score",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | PrimeModelListResponse |

## Create a Prime Model from a Ruleset by project ID

Operation path: `POST /api/v2/projects/{projectId}/primeModels/`

Authentication requirements: `BearerAuth`

Create a Prime model using a particular ruleset.

DataRobot Prime is not available for multiclass projects.

Once rulesets approximating a parent model have been created, using POST /api/v2/projects/(projectId)/models/(modelId)/primeRulesets/, this route will allow creation of a Prime model using one of those rulesets.

Available rulesets can be retrieved via GET /api/v2/projects/(projectId)/models/(modelId)/primeRulesets/.  Deprecated in v2.35.

### Body parameter

```
{
  "properties": {
    "parentModelId": {
      "description": "The model being approximated.",
      "type": "string"
    },
    "rulesetId": {
      "description": "The ID of the ruleset to use.",
      "type": "integer"
    }
  },
  "required": [
    "parentModelId",
    "rulesetId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project in which to create the model. |
| body | body | PrimeModelCreatePayload | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | Prime model creation job successfully added to queue. See the Location header. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | a url that can be polled to check the status of the job |

## Retrieve Prime model details by project ID

Operation path: `GET /api/v2/projects/{projectId}/primeModels/{modelId}/`

Authentication requirements: `BearerAuth`

Retrieve Prime model details.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project to retrieve the model from. |
| modelId | path | string | true | The model to retrieve. |

### Example responses

> 200 Response

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "ruleCount": {
      "description": "The number of rules used to create this model.",
      "type": "integer"
    },
    "rulesetId": {
      "description": "The ID of the ruleset this model uses.",
      "type": "integer"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "score": {
      "description": "The validation score of the model's ruleset.",
      "type": "number"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "ruleCount",
    "rulesetId",
    "samplePct",
    "score",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | Prime models are an extension of models, so the response includes all attributes that would be in a response to GET /api/v2/projects/(projectId)/models/(modelId)/ as well as some additional ones. | PrimeModelDetailsRetrieveResponse |

## List recommended models by project ID

Operation path: `GET /api/v2/projects/{projectId}/recommendedModels/`

Authentication requirements: `BearerAuth`

Retrieves all of the current recommended models for the project.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "items": {
    "properties": {
      "modelId": {
        "description": "The ID of the recommended model.",
        "type": "string"
      },
      "recommendationType": {
        "description": "The type of model recommendation.",
        "enum": [
          "MOSTACCURATE",
          "LIMITEDACCURATE",
          "FASTACCURATE",
          "RECOMMENDEDFORDEPLOYMENT",
          "PREPAREDFORDEPLOYMENT"
        ],
        "type": "string"
      }
    },
    "required": [
      "modelId",
      "recommendationType"
    ],
    "type": "object"
  },
  "type": "array"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The list of recommended models. | Inline |

### Response Schema

Status Code 200

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| anonymous | [RecommendedModelResponse] | false |  | none |
| » modelId | string | true |  | The ID of the recommended model. |
| » recommendationType | string | true |  | The type of model recommendation. |

### Enumerated Values

| Property | Value |
| --- | --- |
| recommendationType | [MOSTACCURATE, LIMITEDACCURATE, FASTACCURATE, RECOMMENDEDFORDEPLOYMENT, PREPAREDFORDEPLOYMENT] |

## Get the recommended model by project ID

Operation path: `GET /api/v2/projects/{projectId}/recommendedModels/recommendedModel/`

Authentication requirements: `BearerAuth`

This route returns the simplest recommended model available. To see all the available recommended models, use [GET /api/v2/projects/{projectId}/recommendedModels/][get-apiv2projectsprojectidrecommendedmodels].

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |

### Example responses

> 200 Response

```
{
  "properties": {
    "modelId": {
      "description": "The ID of the recommended model.",
      "type": "string"
    },
    "recommendationType": {
      "description": "The type of model recommendation.",
      "enum": [
        "MOSTACCURATE",
        "LIMITEDACCURATE",
        "FASTACCURATE",
        "RECOMMENDEDFORDEPLOYMENT",
        "PREPAREDFORDEPLOYMENT"
      ],
      "type": "string"
    }
  },
  "required": [
    "modelId",
    "recommendationType"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The recommended model. | RecommendedModelResponse |

## Get RuleFit code files by project ID

Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/`

Authentication requirements: `BearerAuth`

List all RuleFit code files available for download.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| modelId | query | string | false | If specified, only RuleFit code files with code used in the specified RuleFit model will be returned; otherwise, all applicable RuleFit files will be returned. |
| offset | query | integer | true | This many files will be skipped. |
| limit | query | integer | true | At most this many files are returned. |
| projectId | path | string | true | The project to list available files for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/.",
      "items": {
        "properties": {
          "id": {
            "description": "The ID of the file.",
            "type": "string"
          },
          "isValid": {
            "description": "Whether the code passed basic validation checks.",
            "type": "boolean"
          },
          "language": {
            "description": "The language the code is written in (e.g., Python).",
            "enum": [
              "Python",
              "Java"
            ],
            "type": "string"
          },
          "modelId": {
            "description": "The ID of the RuleFit model.",
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project the file belongs to.",
            "type": "string"
          }
        },
        "required": [
          "id",
          "isValid",
          "language",
          "modelId",
          "projectId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | RuleFitCodeFileListResponse |

## Create a RuleFit code file by project ID

Operation path: `POST /api/v2/projects/{projectId}/ruleFitFiles/`

Authentication requirements: `BearerAuth`

Request creation and validation of source code from a RuleFit model.

### Body parameter

```
{
  "properties": {
    "language": {
      "description": "The desired language of the generated code.",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The RuleFit model to generate code for.",
      "type": "string"
    }
  },
  "required": [
    "language",
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project the file belongs to. |
| body | body | RuleFitCodeFileCreate | false | none |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 202 | Accepted | The RuleFit code validation job was added to the queue. See the Location header. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 202 | Location | string | url | A URL that can be polled to check the status of the RuleFit code validation job. |

## Get RuleFit code file information by project ID

Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/{ruleFitFileId}/`

Authentication requirements: `BearerAuth`

Get information about a RuleFit code file available for download.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| ruleFitFileId | path | string | true | The ID of the file. |
| projectId | path | string | true | The project to list available files for. |

### Example responses

> 200 Response

```
{
  "properties": {
    "id": {
      "description": "The ID of the file.",
      "type": "string"
    },
    "isValid": {
      "description": "Whether the code passed basic validation checks.",
      "type": "boolean"
    },
    "language": {
      "description": "The language the code is written in (e.g., Python).",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the RuleFit model.",
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project the file belongs to.",
      "type": "string"
    }
  },
  "required": [
    "id",
    "isValid",
    "language",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | none | RuleFitCodeFileResponse |

## Download RuleFit code by project ID

Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/{ruleFitFileId}/download/`

Authentication requirements: `BearerAuth`

Download code from an existing RuleFit file.

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| ruleFitFileId | path | string | true | The ID of the file. |
| projectId | path | string | true | The project to list available files for. |

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The response will contain a file with the executable code from the RuleFit model. | None |

### Response Headers

| Status | Header | Type | Format | Description |
| --- | --- | --- | --- | --- |
| 200 | Content-Disposition | string |  | Will be attachment;filename="<"filename">". The suggested filename will depend on the language RuleFit file was generated for. |

## Update champion model by project ID

Operation path: `PUT /api/v2/projects/{projectId}/segmentChampion/`

Authentication requirements: `BearerAuth`

Update champion model for a segment project.

### Body parameter

```
{
  "properties": {
    "clone": {
      "default": false,
      "description": "Clone current combined model and assign champion to the new combined model.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "modelId": {
      "description": "The ID of segment champion model.",
      "type": "string"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Parameters

| Name | In | Type | Required | Description |
| --- | --- | --- | --- | --- |
| projectId | path | string | true | The project ID. |
| body | body | SegmentChampionModelUpdate | false | none |

### Example responses

> 200 Response

```
{
  "properties": {
    "combinedModelId": {
      "description": "The ID of the combined model that has been updated.",
      "type": "string"
    }
  },
  "required": [
    "combinedModelId"
  ],
  "type": "object"
}
```

### Responses

| Status | Meaning | Description | Schema |
| --- | --- | --- | --- |
| 200 | OK | The ID of the combined model that has been updated with the new segment champion model. | SegmentChampionModelUpdateResponse |

# Schemas

## AdvancedTuningArgumentsRetrieveResponse

```
{
  "properties": {
    "tuningDescription": {
      "description": "Human-readable description of the tuned model, if specified by the user. `null` if unspecified.",
      "type": [
        "string",
        "null"
      ]
    },
    "tuningParameters": {
      "description": "An array of objects containing information about tuning parameters that are supported by the specified model.",
      "items": {
        "properties": {
          "constraints": {
            "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.",
            "properties": {
              "ascii": {
                "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "float": {
                "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).",
                "properties": {
                  "max": {
                    "description": "Maximum value for the parameter.",
                    "type": "number"
                  },
                  "min": {
                    "description": "Minimum value for the parameter.",
                    "type": "number"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "max",
                  "min",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "floatList": {
                "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).",
                "properties": {
                  "maxLength": {
                    "description": "Maximum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "maxVal": {
                    "description": "Maximum permitted value.",
                    "type": "number"
                  },
                  "minLength": {
                    "description": "Minimum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "minVal": {
                    "description": "Minimum permitted value.",
                    "type": "number"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "maxLength",
                  "maxVal",
                  "minLength",
                  "minVal",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "int": {
                "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.",
                "properties": {
                  "max": {
                    "description": "Maximum value for the parameter.",
                    "type": "integer"
                  },
                  "min": {
                    "description": "Minimum value for the parameter.",
                    "type": "integer"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "max",
                  "min",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "intList": {
                "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.",
                "properties": {
                  "maxLength": {
                    "description": "Maximum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "maxVal": {
                    "description": "Maximum permitted value.",
                    "type": "integer"
                  },
                  "minLength": {
                    "description": "Minimum permitted length of the list.",
                    "minimum": 0,
                    "type": "integer"
                  },
                  "minVal": {
                    "description": "Minimum permitted value.",
                    "type": "integer"
                  },
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "maxLength",
                  "maxVal",
                  "minLength",
                  "minVal",
                  "supportsGridSearch"
                ],
                "type": "object"
              },
              "select": {
                "description": "Indicates that the value can be one selected from a list of known values.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  },
                  "values": {
                    "description": "List of valid values for this field.",
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                },
                "required": [
                  "supportsGridSearch",
                  "values"
                ],
                "type": "object"
              },
              "selectgrid": {
                "description": "Indicates that the value can be one selected from a list of known values.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  },
                  "values": {
                    "description": "List of valid values for this field.",
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                },
                "required": [
                  "supportsGridSearch",
                  "values"
                ],
                "type": "object"
              },
              "unicode": {
                "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
                "properties": {
                  "supportsGridSearch": {
                    "description": "When True, Grid Search is supported for this parameter.",
                    "type": "boolean"
                  }
                },
                "required": [
                  "supportsGridSearch"
                ],
                "type": "object"
              }
            },
            "type": "object"
          },
          "currentValue": {
            "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          },
          "defaultValue": {
            "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          },
          "parameterId": {
            "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.",
            "type": "string"
          },
          "parameterName": {
            "description": "Name of the parameter.",
            "type": "string"
          },
          "taskName": {
            "description": "Human-readable name of the task that this parameter belongs to.",
            "type": "string"
          },
          "vertexId": {
            "description": "Id of the vertex this parameter belongs to.",
            "type": "string",
            "x-versionadded": "v2.29"
          }
        },
        "required": [
          "constraints",
          "currentValue",
          "defaultValue",
          "parameterId",
          "parameterName",
          "taskName",
          "vertexId"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "tuningDescription",
    "tuningParameters"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| tuningDescription | string,null | true |  | Human-readable description of the tuned model, if specified by the user. null if unspecified. |
| tuningParameters | [TuningParameters] | true |  | An array of objects containing information about tuning parameters that are supported by the specified model. |

## BacktestStatusResponse

```
{
  "properties": {
    "index": {
      "description": "the index of the fold",
      "type": "integer"
    },
    "score": {
      "description": "the score of the model for this backtesting fold, if computed",
      "type": [
        "number",
        "null"
      ]
    },
    "status": {
      "description": "the status of the current backtest model job",
      "enum": [
        "COMPLETED",
        "NOT_COMPLETED",
        "INSUFFICIENT_DATA",
        "ERRORED",
        "BACKTEST_BOUNDARIES_EXCEEDED"
      ],
      "type": "string"
    },
    "trainingDuration": {
      "description": "the duration of the data used to train the model for this backtesting fold",
      "format": "duration",
      "type": "string"
    },
    "trainingEndDate": {
      "description": "the end date of the training for this backtesting fold",
      "format": "date-time",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "the number of rows used to train the model for this backtesting fold",
      "type": "integer"
    },
    "trainingStartDate": {
      "description": "the start date of the training for this backtesting fold",
      "format": "date-time",
      "type": "string"
    }
  },
  "required": [
    "index",
    "score",
    "status",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| index | integer | true |  | the index of the fold |
| score | number,null | true |  | the score of the model for this backtesting fold, if computed |
| status | string | true |  | the status of the current backtest model job |
| trainingDuration | string(duration) | true |  | the duration of the data used to train the model for this backtesting fold |
| trainingEndDate | string(date-time) | true |  | the end date of the training for this backtesting fold |
| trainingRowCount | integer | true |  | the number of rows used to train the model for this backtesting fold |
| trainingStartDate | string(date-time) | true |  | the start date of the training for this backtesting fold |

### Enumerated Values

| Property | Value |
| --- | --- |
| status | [COMPLETED, NOT_COMPLETED, INSUFFICIENT_DATA, ERRORED, BACKTEST_BOUNDARIES_EXCEEDED] |

## BaseConstraintType

```
{
  "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
  "properties": {
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    }
  },
  "required": [
    "supportsGridSearch"
  ],
  "type": "object"
}
```

Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |

## BiasMitigatedModelsDataResponse

```
{
  "properties": {
    "biasMitigationTechnique": {
      "description": "Method applied to perform bias mitigation.",
      "enum": [
        "preprocessingReweighing",
        "postProcessingRejectionOptionBasedClassification"
      ],
      "type": "string",
      "x-versionadded": "v2.27"
    },
    "includeBiasMitigationFeatureAsPredictorVariable": {
      "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.",
      "type": "boolean",
      "x-versionadded": "v2.27"
    },
    "modelId": {
      "description": "Mitigated model ID",
      "type": "string"
    },
    "parentModelId": {
      "description": "Parent model ID",
      "type": [
        "string",
        "null"
      ]
    },
    "protectedFeature": {
      "description": "Protected feature that will be used in a bias mitigation task to mitigate bias",
      "type": "string"
    }
  },
  "required": [
    "biasMitigationTechnique",
    "includeBiasMitigationFeatureAsPredictorVariable",
    "modelId",
    "parentModelId",
    "protectedFeature"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| biasMitigationTechnique | string | true |  | Method applied to perform bias mitigation. |
| includeBiasMitigationFeatureAsPredictorVariable | boolean | true |  | Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task. |
| modelId | string | true |  | Mitigated model ID |
| parentModelId | string,null | true |  | Parent model ID |
| protectedFeature | string | true |  | Protected feature that will be used in a bias mitigation task to mitigate bias |

### Enumerated Values

| Property | Value |
| --- | --- |
| biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] |

## BiasMitigatedModelsListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Retrieve list of mitigated models for project.",
      "items": {
        "properties": {
          "biasMitigationTechnique": {
            "description": "Method applied to perform bias mitigation.",
            "enum": [
              "preprocessingReweighing",
              "postProcessingRejectionOptionBasedClassification"
            ],
            "type": "string",
            "x-versionadded": "v2.27"
          },
          "includeBiasMitigationFeatureAsPredictorVariable": {
            "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.",
            "type": "boolean",
            "x-versionadded": "v2.27"
          },
          "modelId": {
            "description": "Mitigated model ID",
            "type": "string"
          },
          "parentModelId": {
            "description": "Parent model ID",
            "type": [
              "string",
              "null"
            ]
          },
          "protectedFeature": {
            "description": "Protected feature that will be used in a bias mitigation task to mitigate bias",
            "type": "string"
          }
        },
        "required": [
          "biasMitigationTechnique",
          "includeBiasMitigationFeatureAsPredictorVariable",
          "modelId",
          "parentModelId",
          "protectedFeature"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [BiasMitigatedModelsDataResponse] | true |  | Retrieve list of mitigated models for project. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |
| totalCount | integer | true |  | The total number of items across all pages. |

## BiasMitigationModelCreate

```
{
  "properties": {
    "biasMitigationFeature": {
      "description": "The name of the protected feature used to mitigate bias on models.",
      "minLength": 1,
      "type": "string",
      "x-versionadded": "v2.27"
    },
    "biasMitigationParentLid": {
      "description": "The ID of the model to modify with a bias-mitigation task.",
      "type": "string"
    },
    "biasMitigationTechnique": {
      "description": "Method applied to perform bias mitigation.",
      "enum": [
        "preprocessingReweighing",
        "postProcessingRejectionOptionBasedClassification"
      ],
      "type": "string",
      "x-versionadded": "v2.27"
    },
    "includeBiasMitigationFeatureAsPredictorVariable": {
      "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.",
      "type": "boolean",
      "x-versionadded": "v2.27"
    }
  },
  "required": [
    "biasMitigationFeature",
    "biasMitigationParentLid",
    "biasMitigationTechnique",
    "includeBiasMitigationFeatureAsPredictorVariable"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| biasMitigationFeature | string | true | minLength: 1minLength: 1 | The name of the protected feature used to mitigate bias on models. |
| biasMitigationParentLid | string | true |  | The ID of the model to modify with a bias-mitigation task. |
| biasMitigationTechnique | string | true |  | Method applied to perform bias mitigation. |
| includeBiasMitigationFeatureAsPredictorVariable | boolean | true |  | Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task. |

### Enumerated Values

| Property | Value |
| --- | --- |
| biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] |

## BlenderCreate

```
{
  "properties": {
    "blenderMethod": {
      "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".",
      "enum": [
        "PLS",
        "GLM",
        "ENET",
        "AVG",
        "MED",
        "MAE",
        "MAEL1",
        "FORECAST_DISTANCE_AVG",
        "FORECAST_DISTANCE_ENET",
        "MAX",
        "MIN"
      ],
      "type": "string"
    },
    "modelIds": {
      "description": "The list of models to use in blender.",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "blenderMethod",
    "modelIds"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderMethod | string | true |  | The blender method, one of "PLS", "GLM", "AVG", "ENET", "MED", "MAE", "MAEL1", "TF", "RF", "LGBM", "FORECAST_DISTANCE_ENET" (new in v2.18), "FORECAST_DISTANCE_AVG" (new in v2.18), "MIN", "MAX". |
| modelIds | [string] | true | minItems: 1 | The list of models to use in blender. |

### Enumerated Values

| Property | Value |
| --- | --- |
| blenderMethod | [PLS, GLM, ENET, AVG, MED, MAE, MAEL1, FORECAST_DISTANCE_AVG, FORECAST_DISTANCE_ENET, MAX, MIN] |

## BlenderInfoRetrieveResponse

```
{
  "properties": {
    "blendable": {
      "description": "If True, the models can be blended.",
      "type": "boolean"
    },
    "reason": {
      "description": "Useful info as to why a model can't be blended.",
      "type": "string"
    }
  },
  "required": [
    "blendable",
    "reason"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blendable | boolean | true |  | If True, the models can be blended. |
| reason | string | true |  | Useful info as to why a model can't be blended. |

## BlenderListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid].",
      "items": {
        "properties": {
          "blenderMethod": {
            "description": "Method used to blend results of underlying models.",
            "type": "string"
          },
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelIds": {
            "description": "List of models used in blender.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderMethod",
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelIds",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [BlenderRetrieveResponse] | true |  | Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid]. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |

## BlenderRetrieveResponse

```
{
  "properties": {
    "blenderMethod": {
      "description": "Method used to blend results of underlying models.",
      "type": "string"
    },
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelIds": {
      "description": "List of models used in blender.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderMethod",
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelIds",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderMethod | string | true |  | Method used to blend results of underlying models. |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| hasFinetuners | boolean | false |  | Whether a model has fine tuners. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isAugmented | boolean | false |  | Whether a model was trained using augmentation. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isNClustersDynamicallyDetermined | boolean | false |  | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| lifecycle | ModelLifecycle | true |  | Object returning model lifecycle. |
| linkFunction | string,null | true |  | The link function the final modeler uses in the blueprint. If no link function exists, returns null. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The family the model belongs to, e.g., SVM, GBM, etc. |
| modelFamilyFullName | string | true |  | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. |
| modelIds | [string] | true |  | List of models used in blender. |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| monotonicDecreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer,null | false |  | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. |
| predictionThresholdReadOnly | boolean | true |  | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| supportsComposableMl | boolean | true |  | indicates whether this model is supported in Composable ML. |
| supportsMonotonicConstraints | boolean | true |  | whether this model supports enforcing monotonic constraints |
| timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplingMethod | [random, latest] |

## ClassificationBinDataResponse

```
{
  "properties": {
    "binEnd": {
      "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.",
      "type": "number"
    },
    "binStart": {
      "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.",
      "type": "number"
    },
    "negatives": {
      "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.",
      "type": "integer"
    },
    "positives": {
      "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.",
      "type": "integer"
    }
  },
  "required": [
    "binEnd",
    "binStart",
    "negatives",
    "positives"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| binEnd | number | true |  | The end of the numeric range for the current bin. Note that binEnd - binStart should be a constant, modulo floating-point rounding error, for all bins in a single plot. |
| binStart | number | true |  | The start of the numeric range for the current bin. Must be equal to the binEnd of the previous bin. |
| negatives | integer | true |  | The number of records in the dataset where the model's predicted value falls into this bin and the target is negative. |
| positives | integer | true |  | The number of records in the dataset where the model's predicted value falls into this bin and the target is positive. |

## ClusterInfoList

```
{
  "properties": {
    "name": {
      "description": "A cluster name.",
      "maxLength": 50,
      "minLength": 1,
      "type": "string"
    },
    "percent": {
      "description": "The percentage of rows in the dataset this cluster contains.",
      "maximum": 100,
      "minimum": 0,
      "type": "number"
    }
  },
  "required": [
    "name"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| name | string | true | maxLength: 50minLength: 1minLength: 1 | A cluster name. |
| percent | number | false | maximum: 100minimum: 0 | The percentage of rows in the dataset this cluster contains. |

## ClusterNamesMappingValidation

```
{
  "properties": {
    "currentName": {
      "description": "Current cluster name.",
      "maxLength": 50,
      "minLength": 1,
      "type": "string"
    },
    "newName": {
      "description": "New cluster name.",
      "maxLength": 50,
      "minLength": 1,
      "type": "string"
    }
  },
  "required": [
    "currentName",
    "newName"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| currentName | string | true | maxLength: 50minLength: 1minLength: 1 | Current cluster name. |
| newName | string | true | maxLength: 50minLength: 1minLength: 1 | New cluster name. |

## ClusterNamesResponse

```
{
  "properties": {
    "clusters": {
      "description": "A list of the model's cluster information entries.",
      "items": {
        "properties": {
          "name": {
            "description": "A cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          },
          "percent": {
            "description": "The percentage of rows in the dataset this cluster contains.",
            "maximum": 100,
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "name"
        ],
        "type": "object"
      },
      "maxItems": 100,
      "minItems": 2,
      "type": "array"
    },
    "modelId": {
      "description": "The model ID",
      "type": "string"
    },
    "projectId": {
      "description": "The project ID",
      "type": "string"
    }
  },
  "required": [
    "clusters",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| clusters | [ClusterInfoList] | true | maxItems: 100minItems: 2 | A list of the model's cluster information entries. |
| modelId | string | true |  | The model ID |
| projectId | string | true |  | The project ID |

## ClusterNamesUpdateParam

```
{
  "properties": {
    "clusterNameMappings": {
      "description": "\n            A list of the mappings from a cluster's current name to its new name.\n            After update, value passed as a new name will become cluster's current name.\n            All cluster names should be unique and should identify one and only one cluster.\n            ",
      "items": {
        "properties": {
          "currentName": {
            "description": "Current cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          },
          "newName": {
            "description": "New cluster name.",
            "maxLength": 50,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "currentName",
          "newName"
        ],
        "type": "object"
      },
      "maxItems": 100,
      "type": "array"
    }
  },
  "required": [
    "clusterNameMappings"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| clusterNameMappings | [ClusterNamesMappingValidation] | true | maxItems: 100 | A list of the mappings from a cluster's current name to its new name. After update, value passed as a new name will become cluster's current name. All cluster names should be unique and should identify one and only one cluster. |

## CombinedModelListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "The list of combined models.",
      "items": {
        "properties": {
          "combinedModelId": {
            "description": "The ID of the combined model.",
            "type": "string"
          },
          "isActiveCombinedModel": {
            "default": false,
            "description": "Indicates whether this model is the active one in segmented modeling project.",
            "type": "boolean",
            "x-versionadded": "v2.29"
          },
          "modelCategory": {
            "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
            "enum": [
              "combined"
            ],
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project.",
            "type": "string"
          },
          "segmentationTaskId": {
            "description": "The ID of the segmentation task used to generate this combined model.",
            "type": "string"
          }
        },
        "required": [
          "combinedModelId",
          "isActiveCombinedModel",
          "modelCategory",
          "projectId",
          "segmentationTaskId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [CommonGetAndListCombinedModel] | true |  | The list of combined models. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |
| totalCount | integer | true |  | The total number of items across all pages. |

## CombinedModelResponse

```
{
  "properties": {
    "combinedModelId": {
      "description": "The ID of the combined model.",
      "type": "string"
    },
    "isActiveCombinedModel": {
      "default": false,
      "description": "Indicates whether this model is the active one in segmented modeling project.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "modelCategory": {
      "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
      "enum": [
        "combined"
      ],
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project.",
      "type": "string"
    },
    "segmentationTaskId": {
      "description": "The ID of the segmentation task used to generate this combined model.",
      "type": "string"
    },
    "segments": {
      "description": "Information for each segment. Maps each segment to the project and model used for it.",
      "items": {
        "properties": {
          "modelId": {
            "description": "The ID of the segment champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectId": {
            "description": "The ID of the project used for this segment.",
            "type": [
              "string",
              "null"
            ]
          },
          "segment": {
            "description": "Segment name.",
            "type": "string"
          }
        },
        "required": [
          "modelId",
          "projectId",
          "segment"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "combinedModelId",
    "isActiveCombinedModel",
    "modelCategory",
    "projectId",
    "segmentationTaskId",
    "segments"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| combinedModelId | string | true |  | The ID of the combined model. |
| isActiveCombinedModel | boolean | true |  | Indicates whether this model is the active one in segmented modeling project. |
| modelCategory | string | true |  | Indicates what kind of model this is. Will be combined for combined models. |
| projectId | string | true |  | The ID of the project. |
| segmentationTaskId | string | true |  | The ID of the segmentation task used to generate this combined model. |
| segments | [SegmentProjectModelResponse] | true |  | Information for each segment. Maps each segment to the project and model used for it. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | combined |

## CombinedModelSegmentsPaginatedResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "The list of combined model segment info.",
      "items": {
        "properties": {
          "autopilotDone": {
            "description": "Whether autopilot is done for the project.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "holdoutUnlocked": {
            "description": "Whether holdout is unlocked for the project.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "isFrozen": {
            "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "modelAssignedBy": {
            "description": "Who assigned the model as segment champion. The default is ``DataRobot``.",
            "type": [
              "string",
              "null"
            ]
          },
          "modelAwardTime": {
            "description": "The time when the model was awarded as segment champion.",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "modelCount": {
            "description": "The count of trained models in the project.",
            "type": [
              "integer",
              "null"
            ]
          },
          "modelIcon": {
            "description": "The number for the icon representing the given champion model.",
            "items": {
              "type": "integer"
            },
            "type": "array"
          },
          "modelId": {
            "description": "The ID of the segment champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "modelMetrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.",
            "type": [
              "object",
              "null"
            ]
          },
          "modelType": {
            "description": "The description of the model type of the given champion model.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectId": {
            "description": "The ID of the project.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectPaused": {
            "description": "Whether the project is paused right now.",
            "type": [
              "boolean",
              "null"
            ]
          },
          "projectStage": {
            "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.",
            "enum": [
              "modeling",
              "aim",
              "fasteda",
              "eda",
              "eda2",
              "empty"
            ],
            "type": [
              "string",
              "null"
            ]
          },
          "projectStageDescription": {
            "description": "A description of the current stage of the project.",
            "type": [
              "string",
              "null"
            ]
          },
          "projectStatusError": {
            "description": "Project status error message.",
            "type": [
              "string",
              "null"
            ]
          },
          "rowCount": {
            "description": "The count of rows in the project's dataset.",
            "type": [
              "integer",
              "null"
            ]
          },
          "rowPercentage": {
            "description": "The percentage of rows in the segment project's dataset compared to the original dataset.",
            "type": [
              "number",
              "null"
            ]
          },
          "segment": {
            "description": "Segment name.",
            "type": "string"
          }
        },
        "required": [
          "autopilotDone",
          "holdoutUnlocked",
          "isFrozen",
          "modelAssignedBy",
          "modelAwardTime",
          "modelCount",
          "modelIcon",
          "modelId",
          "modelMetrics",
          "modelType",
          "projectId",
          "projectStage",
          "projectStageDescription",
          "rowCount",
          "rowPercentage",
          "segment"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [CombinedModelSegmentsResponse] | true |  | The list of combined model segment info. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |
| totalCount | integer | true |  | The total number of items across all pages. |

## CombinedModelSegmentsResponse

```
{
  "properties": {
    "autopilotDone": {
      "description": "Whether autopilot is done for the project.",
      "type": [
        "boolean",
        "null"
      ]
    },
    "holdoutUnlocked": {
      "description": "Whether holdout is unlocked for the project.",
      "type": [
        "boolean",
        "null"
      ]
    },
    "isFrozen": {
      "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": [
        "boolean",
        "null"
      ]
    },
    "modelAssignedBy": {
      "description": "Who assigned the model as segment champion. The default is ``DataRobot``.",
      "type": [
        "string",
        "null"
      ]
    },
    "modelAwardTime": {
      "description": "The time when the model was awarded as segment champion.",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "modelCount": {
      "description": "The count of trained models in the project.",
      "type": [
        "integer",
        "null"
      ]
    },
    "modelIcon": {
      "description": "The number for the icon representing the given champion model.",
      "items": {
        "type": "integer"
      },
      "type": "array"
    },
    "modelId": {
      "description": "The ID of the segment champion model.",
      "type": [
        "string",
        "null"
      ]
    },
    "modelMetrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.",
      "type": [
        "object",
        "null"
      ]
    },
    "modelType": {
      "description": "The description of the model type of the given champion model.",
      "type": [
        "string",
        "null"
      ]
    },
    "projectId": {
      "description": "The ID of the project.",
      "type": [
        "string",
        "null"
      ]
    },
    "projectPaused": {
      "description": "Whether the project is paused right now.",
      "type": [
        "boolean",
        "null"
      ]
    },
    "projectStage": {
      "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.",
      "enum": [
        "modeling",
        "aim",
        "fasteda",
        "eda",
        "eda2",
        "empty"
      ],
      "type": [
        "string",
        "null"
      ]
    },
    "projectStageDescription": {
      "description": "A description of the current stage of the project.",
      "type": [
        "string",
        "null"
      ]
    },
    "projectStatusError": {
      "description": "Project status error message.",
      "type": [
        "string",
        "null"
      ]
    },
    "rowCount": {
      "description": "The count of rows in the project's dataset.",
      "type": [
        "integer",
        "null"
      ]
    },
    "rowPercentage": {
      "description": "The percentage of rows in the segment project's dataset compared to the original dataset.",
      "type": [
        "number",
        "null"
      ]
    },
    "segment": {
      "description": "Segment name.",
      "type": "string"
    }
  },
  "required": [
    "autopilotDone",
    "holdoutUnlocked",
    "isFrozen",
    "modelAssignedBy",
    "modelAwardTime",
    "modelCount",
    "modelIcon",
    "modelId",
    "modelMetrics",
    "modelType",
    "projectId",
    "projectStage",
    "projectStageDescription",
    "rowCount",
    "rowPercentage",
    "segment"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| autopilotDone | boolean,null | true |  | Whether autopilot is done for the project. |
| holdoutUnlocked | boolean,null | true |  | Whether holdout is unlocked for the project. |
| isFrozen | boolean,null | true |  | Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model. |
| modelAssignedBy | string,null | true |  | Who assigned the model as segment champion. The default is DataRobot. |
| modelAwardTime | string,null(date-time) | true |  | The time when the model was awarded as segment champion. |
| modelCount | integer,null | true |  | The count of trained models in the project. |
| modelIcon | [integer] | true |  | The number for the icon representing the given champion model. |
| modelId | string,null | true |  | The ID of the segment champion model. |
| modelMetrics | object,null | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelType | string,null | true |  | The description of the model type of the given champion model. |
| projectId | string,null | true |  | The ID of the project. |
| projectPaused | boolean,null | false |  | Whether the project is paused right now. |
| projectStage | string,null | true |  | The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed. |
| projectStageDescription | string,null | true |  | A description of the current stage of the project. |
| projectStatusError | string,null | false |  | Project status error message. |
| rowCount | integer,null | true |  | The count of rows in the project's dataset. |
| rowPercentage | number,null | true |  | The percentage of rows in the segment project's dataset compared to the original dataset. |
| segment | string | true |  | Segment name. |

### Enumerated Values

| Property | Value |
| --- | --- |
| projectStage | [modeling, aim, fasteda, eda, eda2, empty] |

## CommonGetAndListCombinedModel

```
{
  "properties": {
    "combinedModelId": {
      "description": "The ID of the combined model.",
      "type": "string"
    },
    "isActiveCombinedModel": {
      "default": false,
      "description": "Indicates whether this model is the active one in segmented modeling project.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "modelCategory": {
      "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
      "enum": [
        "combined"
      ],
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project.",
      "type": "string"
    },
    "segmentationTaskId": {
      "description": "The ID of the segmentation task used to generate this combined model.",
      "type": "string"
    }
  },
  "required": [
    "combinedModelId",
    "isActiveCombinedModel",
    "modelCategory",
    "projectId",
    "segmentationTaskId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| combinedModelId | string | true |  | The ID of the combined model. |
| isActiveCombinedModel | boolean | true |  | Indicates whether this model is the active one in segmented modeling project. |
| modelCategory | string | true |  | Indicates what kind of model this is. Will be combined for combined models. |
| projectId | string | true |  | The ID of the project. |
| segmentationTaskId | string | true |  | The ID of the segmentation task used to generate this combined model. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | combined |

## Constraints

```
{
  "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.",
  "properties": {
    "ascii": {
      "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
      "properties": {
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "supportsGridSearch"
      ],
      "type": "object"
    },
    "float": {
      "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).",
      "properties": {
        "max": {
          "description": "Maximum value for the parameter.",
          "type": "number"
        },
        "min": {
          "description": "Minimum value for the parameter.",
          "type": "number"
        },
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "max",
        "min",
        "supportsGridSearch"
      ],
      "type": "object"
    },
    "floatList": {
      "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).",
      "properties": {
        "maxLength": {
          "description": "Maximum permitted length of the list.",
          "minimum": 0,
          "type": "integer"
        },
        "maxVal": {
          "description": "Maximum permitted value.",
          "type": "number"
        },
        "minLength": {
          "description": "Minimum permitted length of the list.",
          "minimum": 0,
          "type": "integer"
        },
        "minVal": {
          "description": "Minimum permitted value.",
          "type": "number"
        },
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "maxLength",
        "maxVal",
        "minLength",
        "minVal",
        "supportsGridSearch"
      ],
      "type": "object"
    },
    "int": {
      "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.",
      "properties": {
        "max": {
          "description": "Maximum value for the parameter.",
          "type": "integer"
        },
        "min": {
          "description": "Minimum value for the parameter.",
          "type": "integer"
        },
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "max",
        "min",
        "supportsGridSearch"
      ],
      "type": "object"
    },
    "intList": {
      "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.",
      "properties": {
        "maxLength": {
          "description": "Maximum permitted length of the list.",
          "minimum": 0,
          "type": "integer"
        },
        "maxVal": {
          "description": "Maximum permitted value.",
          "type": "integer"
        },
        "minLength": {
          "description": "Minimum permitted length of the list.",
          "minimum": 0,
          "type": "integer"
        },
        "minVal": {
          "description": "Minimum permitted value.",
          "type": "integer"
        },
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "maxLength",
        "maxVal",
        "minLength",
        "minVal",
        "supportsGridSearch"
      ],
      "type": "object"
    },
    "select": {
      "description": "Indicates that the value can be one selected from a list of known values.",
      "properties": {
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        },
        "values": {
          "description": "List of valid values for this field.",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "supportsGridSearch",
        "values"
      ],
      "type": "object"
    },
    "selectgrid": {
      "description": "Indicates that the value can be one selected from a list of known values.",
      "properties": {
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        },
        "values": {
          "description": "List of valid values for this field.",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "supportsGridSearch",
        "values"
      ],
      "type": "object"
    },
    "unicode": {
      "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
      "properties": {
        "supportsGridSearch": {
          "description": "When True, Grid Search is supported for this parameter.",
          "type": "boolean"
        }
      },
      "required": [
        "supportsGridSearch"
      ],
      "type": "object"
    }
  },
  "type": "object"
}
```

Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| ascii | BaseConstraintType | false |  | Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that ascii fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines. |
| float | Float | false |  | Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point). |
| floatList | FloatList | false |  | Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point). |
| int | Int | false |  | Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer. |
| intList | IntList | false |  | Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers. |
| select | Select | false |  | Indicates that the value can be one selected from a list of known values. |
| selectgrid | Select | false |  | Indicates that the value can be one selected from a list of known values. |
| unicode | BaseConstraintType | false |  | Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that ascii fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines. |

## CrossValidationRetrieveResponse

```
{
  "properties": {
    "cvScores": {
      "description": "A dictionary `cvScores` with sub-dictionary keyed by `partition_id`, each `partition_id` is itself a dictionary keyed by `metric_name` where the value is the reading for that particular metric for the partition_id.",
      "example": "\n        {\n            \"cvScores\": {\n                \"FVE Gamma\": {\n                    \"0.0\": 0.24334,\n                    \"1.0\": 0.17757,\n                    \"2.0\": 0.21803,\n                    \"3.0\": 0.20185,\n                    \"4.0\": 0.20576\n                },\n                \"FVE Poisson\": {\n                    \"0.0\": 0.24527,\n                    \"1.0\": 0.22092,\n                    \"2.0\": 0.22451,\n                    \"3.0\": 0.24417,\n                    \"4.0\": 0.21654\n                }\n            }\n        }\n",
      "type": "object"
    }
  },
  "required": [
    "cvScores"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| cvScores | object | true |  | A dictionary cvScores with sub-dictionary keyed by partition_id, each partition_id is itself a dictionary keyed by metric_name where the value is the reading for that particular metric for the partition_id. |

## DatetimeModelDetailsResponse

```
{
  "properties": {
    "backtests": {
      "description": "information on each backtesting fold of the model",
      "items": {
        "properties": {
          "index": {
            "description": "the index of the fold",
            "type": "integer"
          },
          "score": {
            "description": "the score of the model for this backtesting fold, if computed",
            "type": [
              "number",
              "null"
            ]
          },
          "status": {
            "description": "the status of the current backtest model job",
            "enum": [
              "COMPLETED",
              "NOT_COMPLETED",
              "INSUFFICIENT_DATA",
              "ERRORED",
              "BACKTEST_BOUNDARIES_EXCEEDED"
            ],
            "type": "string"
          },
          "trainingDuration": {
            "description": "the duration of the data used to train the model for this backtesting fold",
            "format": "duration",
            "type": "string"
          },
          "trainingEndDate": {
            "description": "the end date of the training for this backtesting fold",
            "format": "date-time",
            "type": "string"
          },
          "trainingRowCount": {
            "description": "the number of rows used to train the model for this backtesting fold",
            "type": "integer"
          },
          "trainingStartDate": {
            "description": "the start date of the training for this backtesting fold",
            "format": "date-time",
            "type": "string"
          }
        },
        "required": [
          "index",
          "score",
          "status",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "effectiveFeatureDerivationWindowEnd": {
      "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.",
      "maximum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "effectiveFeatureDerivationWindowStart": {
      "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.",
      "exclusiveMaximum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "forecastWindowEnd": {
      "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "forecastWindowStart": {
      "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.16"
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "holdoutScore": {
      "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked",
      "type": [
        "number",
        "null"
      ]
    },
    "holdoutStatus": {
      "description": "the status of the holdout fold",
      "enum": [
        "COMPLETED",
        "INSUFFICIENT_DATA",
        "HOLDOUT_BOUNDARIES_EXCEEDED"
      ],
      "type": "string"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training.  The `backtesting` and  `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.",
      "example": "\n        {\n            \"metrics\": {\n                \"FVE Poisson\": {\n                    \"holdout\": null,\n                    \"validation\": 0.56269,\n                    \"backtesting\": 0.50166,\n                    \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n                    \"crossValidation\": null\n                },\n                \"RMSE\": {\n                    \"holdout\": null,\n                    \"validation\": 21.0836,\n                    \"backtesting\": 23.361932,\n                    \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n                    \"crossValidation\": null\n                }\n            }\n        }\n",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "always null for datetime models",
      "enum": [
        null
      ],
      "type": "string"
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingInfo": {
      "description": "holdout and prediction training data details",
      "properties": {
        "holdoutTrainingDuration": {
          "description": "the duration of the data used to train a model to score the holdout",
          "format": "duration",
          "type": "string"
        },
        "holdoutTrainingEndDate": {
          "description": "the end date of the data used to train a model to score the holdout",
          "format": "date-time",
          "type": "string"
        },
        "holdoutTrainingRowCount": {
          "description": "the number of rows used to train a model to score the holdout",
          "type": "integer"
        },
        "holdoutTrainingStartDate": {
          "description": "the start date of data used to train a model to score the holdout",
          "format": "date-time",
          "type": "string"
        },
        "predictionTrainingDuration": {
          "description": "the duration of the data used to train a model to make predictions",
          "format": "duration",
          "type": "string"
        },
        "predictionTrainingEndDate": {
          "description": "the end date of the data used to train a model to make predictions",
          "format": "date-time",
          "type": "string"
        },
        "predictionTrainingRowCount": {
          "description": "the number of rows used to train a model to make predictions",
          "type": "integer"
        },
        "predictionTrainingStartDate": {
          "description": "the start date of data used to train a model to make predictions",
          "format": "date-time",
          "type": "string"
        }
      },
      "required": [
        "holdoutTrainingDuration",
        "holdoutTrainingEndDate",
        "holdoutTrainingRowCount",
        "holdoutTrainingStartDate",
        "predictionTrainingDuration",
        "predictionTrainingEndDate",
        "predictionTrainingRowCount",
        "predictionTrainingStartDate"
      ],
      "type": "object"
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "windowsBasisUnit": {
      "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.",
      "enum": [
        "MILLISECOND",
        "SECOND",
        "MINUTE",
        "HOUR",
        "DAY",
        "WEEK",
        "MONTH",
        "QUARTER",
        "YEAR",
        "ROW"
      ],
      "type": "string",
      "x-versionadded": "v2.16"
    }
  },
  "required": [
    "backtests",
    "blenderModels",
    "blueprintId",
    "effectiveFeatureDerivationWindowEnd",
    "effectiveFeatureDerivationWindowStart",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "forecastWindowEnd",
    "forecastWindowStart",
    "frozenPct",
    "hasCodegen",
    "holdoutScore",
    "holdoutStatus",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingInfo",
    "trainingRowCount",
    "trainingStartDate",
    "windowsBasisUnit"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| backtests | [BacktestStatusResponse] | true |  | information on each backtesting fold of the model |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| effectiveFeatureDerivationWindowEnd | integer | true | maximum: 0 | Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end. |
| effectiveFeatureDerivationWindowStart | integer | true |  | Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the featureDerivationWindowStart set on the project due to the differencing method and period selected. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| forecastWindowEnd | integer | true | minimum: 0 | Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end. |
| forecastWindowStart | integer | true | minimum: 0 | Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| hasFinetuners | boolean | false |  | Whether a model has fine tuners. |
| holdoutScore | number,null | true |  | the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked |
| holdoutStatus | string | true |  | the status of the holdout fold |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isAugmented | boolean | false |  | Whether a model was trained using augmentation. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isNClustersDynamicallyDetermined | boolean | false |  | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| lifecycle | ModelLifecycle | true |  | Object returning model lifecycle. |
| linkFunction | string,null | true |  | The link function the final modeler uses in the blueprint. If no link function exists, returns null. |
| metrics | object | true |  | Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The validation score will be the score of the first backtest, which will be computed during initial model training. The backtesting and backtestingScores scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The backtesting score is the average score across all backtests. The backtestingScores is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The holdout score is the score against the holdout data, using the training data defined in trainingInfo. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The family the model belongs to, e.g., SVM, GBM, etc. |
| modelFamilyFullName | string | true |  | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| monotonicDecreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer,null | false |  | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. |
| predictionThresholdReadOnly | boolean | true |  | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | string | true |  | always null for datetime models |
| samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| supportsComposableMl | boolean | true |  | indicates whether this model is supported in Composable ML. |
| supportsMonotonicConstraints | boolean | true |  | whether this model supports enforcing monotonic constraints |
| timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| trainingInfo | TrainingInfoResponse | true |  | holdout and prediction training data details |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |
| windowsBasisUnit | string | true |  | Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window. |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| holdoutStatus | [COMPLETED, INSUFFICIENT_DATA, HOLDOUT_BOUNDARIES_EXCEEDED] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplePct | null |
| samplingMethod | [random, latest] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |

## DatetimeModelRecordResponse

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object",
      "x-versionadded": "v2.33"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelFamily": {
      "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.33"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelNumber",
    "modelType",
    "parentModelId",
    "processes",
    "projectId",
    "samplePct",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplingMethod | [random, latest] |

## DatetimeModelSubmissionResponse

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| message | string | true |  | Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. |

## DatetimeModelsResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid].",
      "items": {
        "properties": {
          "backtests": {
            "description": "information on each backtesting fold of the model",
            "items": {
              "properties": {
                "index": {
                  "description": "the index of the fold",
                  "type": "integer"
                },
                "score": {
                  "description": "the score of the model for this backtesting fold, if computed",
                  "type": [
                    "number",
                    "null"
                  ]
                },
                "status": {
                  "description": "the status of the current backtest model job",
                  "enum": [
                    "COMPLETED",
                    "NOT_COMPLETED",
                    "INSUFFICIENT_DATA",
                    "ERRORED",
                    "BACKTEST_BOUNDARIES_EXCEEDED"
                  ],
                  "type": "string"
                },
                "trainingDuration": {
                  "description": "the duration of the data used to train the model for this backtesting fold",
                  "format": "duration",
                  "type": "string"
                },
                "trainingEndDate": {
                  "description": "the end date of the training for this backtesting fold",
                  "format": "date-time",
                  "type": "string"
                },
                "trainingRowCount": {
                  "description": "the number of rows used to train the model for this backtesting fold",
                  "type": "integer"
                },
                "trainingStartDate": {
                  "description": "the start date of the training for this backtesting fold",
                  "format": "date-time",
                  "type": "string"
                }
              },
              "required": [
                "index",
                "score",
                "status",
                "trainingDuration",
                "trainingEndDate",
                "trainingRowCount",
                "trainingStartDate"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "effectiveFeatureDerivationWindowEnd": {
            "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.",
            "maximum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "effectiveFeatureDerivationWindowStart": {
            "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.",
            "exclusiveMaximum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "forecastWindowEnd": {
            "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.",
            "minimum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "forecastWindowStart": {
            "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.",
            "minimum": 0,
            "type": "integer",
            "x-versionadded": "v2.16"
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "holdoutScore": {
            "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked",
            "type": [
              "number",
              "null"
            ]
          },
          "holdoutStatus": {
            "description": "the status of the holdout fold",
            "enum": [
              "COMPLETED",
              "INSUFFICIENT_DATA",
              "HOLDOUT_BOUNDARIES_EXCEEDED"
            ],
            "type": "string"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training.  The `backtesting` and  `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.",
            "example": "\n        {\n            \"metrics\": {\n                \"FVE Poisson\": {\n                    \"holdout\": null,\n                    \"validation\": 0.56269,\n                    \"backtesting\": 0.50166,\n                    \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n                    \"crossValidation\": null\n                },\n                \"RMSE\": {\n                    \"holdout\": null,\n                    \"validation\": 21.0836,\n                    \"backtesting\": 23.361932,\n                    \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n                    \"crossValidation\": null\n                }\n            }\n        }\n",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "always null for datetime models",
            "enum": [
              null
            ],
            "type": "string"
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingInfo": {
            "description": "holdout and prediction training data details",
            "properties": {
              "holdoutTrainingDuration": {
                "description": "the duration of the data used to train a model to score the holdout",
                "format": "duration",
                "type": "string"
              },
              "holdoutTrainingEndDate": {
                "description": "the end date of the data used to train a model to score the holdout",
                "format": "date-time",
                "type": "string"
              },
              "holdoutTrainingRowCount": {
                "description": "the number of rows used to train a model to score the holdout",
                "type": "integer"
              },
              "holdoutTrainingStartDate": {
                "description": "the start date of data used to train a model to score the holdout",
                "format": "date-time",
                "type": "string"
              },
              "predictionTrainingDuration": {
                "description": "the duration of the data used to train a model to make predictions",
                "format": "duration",
                "type": "string"
              },
              "predictionTrainingEndDate": {
                "description": "the end date of the data used to train a model to make predictions",
                "format": "date-time",
                "type": "string"
              },
              "predictionTrainingRowCount": {
                "description": "the number of rows used to train a model to make predictions",
                "type": "integer"
              },
              "predictionTrainingStartDate": {
                "description": "the start date of data used to train a model to make predictions",
                "format": "date-time",
                "type": "string"
              }
            },
            "required": [
              "holdoutTrainingDuration",
              "holdoutTrainingEndDate",
              "holdoutTrainingRowCount",
              "holdoutTrainingStartDate",
              "predictionTrainingDuration",
              "predictionTrainingEndDate",
              "predictionTrainingRowCount",
              "predictionTrainingStartDate"
            ],
            "type": "object"
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "windowsBasisUnit": {
            "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.",
            "enum": [
              "MILLISECOND",
              "SECOND",
              "MINUTE",
              "HOUR",
              "DAY",
              "WEEK",
              "MONTH",
              "QUARTER",
              "YEAR",
              "ROW"
            ],
            "type": "string",
            "x-versionadded": "v2.16"
          }
        },
        "required": [
          "backtests",
          "blenderModels",
          "blueprintId",
          "effectiveFeatureDerivationWindowEnd",
          "effectiveFeatureDerivationWindowStart",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "forecastWindowEnd",
          "forecastWindowStart",
          "frozenPct",
          "hasCodegen",
          "holdoutScore",
          "holdoutStatus",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingInfo",
          "trainingRowCount",
          "trainingStartDate",
          "windowsBasisUnit"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [DatetimeModelDetailsResponse] | true |  | each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid]. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |

## DerivedFeatures

```
{
  "properties": {
    "coefficient": {
      "description": "The coefficient for this feature.",
      "type": "number"
    },
    "derivedFeature": {
      "description": "The name of the derived feature.",
      "type": "string"
    },
    "originalFeature": {
      "description": "The name of the feature used to derive this feature.",
      "type": "string"
    },
    "stageCoefficients": {
      "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).",
      "items": {
        "properties": {
          "coefficient": {
            "description": "The corresponding value of the coefficient for that stage.",
            "type": "number"
          },
          "stage": {
            "description": "The name of the stage.",
            "type": "string"
          }
        },
        "required": [
          "coefficient",
          "stage"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "transformations": {
      "description": "An array of json objects describing the transformations applied to create this derived feature.",
      "items": {
        "properties": {
          "name": {
            "description": "The name of the transformation.",
            "type": "string"
          },
          "value": {
            "description": "The value used in carrying it out.",
            "type": "string"
          }
        },
        "required": [
          "name",
          "value"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "type": {
      "description": "The type of this feature.",
      "type": "string"
    }
  },
  "required": [
    "coefficient",
    "derivedFeature",
    "originalFeature",
    "stageCoefficients",
    "transformations",
    "type"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| coefficient | number | true |  | The coefficient for this feature. |
| derivedFeature | string | true |  | The name of the derived feature. |
| originalFeature | string | true |  | The name of the feature used to derive this feature. |
| stageCoefficients | [StageCoefficients] | true |  | An array of json objects describing separate coefficients for every stage of model (empty for single stage models). |
| transformations | [Transformations] | true |  | An array of json objects describing the transformations applied to create this derived feature. |
| type | string | true |  | The type of this feature. |

## Empty

```
{
  "type": "object"
}
```

### Properties

None

## EureqaDistributionDetailResponse

```
{
  "properties": {
    "bins": {
      "description": "The distribution plot data.",
      "items": {
        "properties": {
          "binEnd": {
            "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.",
            "type": "number"
          },
          "binStart": {
            "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.",
            "type": "number"
          },
          "negatives": {
            "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.",
            "type": "integer"
          },
          "positives": {
            "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.",
            "type": "integer"
          }
        },
        "required": [
          "binEnd",
          "binStart",
          "negatives",
          "positives"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "complexity": {
      "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
      "type": "integer"
    },
    "error": {
      "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
      "type": [
        "number",
        "null"
      ]
    },
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "eureqaSolutionId": {
      "description": "The ID of the solution.",
      "type": "string"
    },
    "expression": {
      "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
      "type": "string"
    },
    "expressionAnnotated": {
      "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
      "type": "string"
    },
    "threshold": {
      "description": "Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the `binStart` of the first bin and `binEnd` of the last bin.",
      "type": "number"
    }
  },
  "required": [
    "bins",
    "complexity",
    "error",
    "errorMetric",
    "eureqaSolutionId",
    "expression",
    "expressionAnnotated",
    "threshold"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| bins | [ClassificationBinDataResponse] | true |  | The distribution plot data. |
| complexity | integer | true |  | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. |
| error | number,null | true |  | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. |
| errorMetric | string | true |  | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. |
| eureqaSolutionId | string | true |  | The ID of the solution. |
| expression | string | true |  | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. |
| expressionAnnotated | string | true |  | The expression, rendered with additional tags to assist in automatic parsing. |
| threshold | number | true |  | Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the binStart of the first bin and binEnd of the last bin. |

## EureqaLeaderboardEntryPayload

```
{
  "properties": {
    "parentModelId": {
      "description": "The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with `solutionId`.",
      "type": "string"
    },
    "solutionId": {
      "description": "The ID of the solution to be cloned.",
      "type": "string"
    }
  },
  "required": [
    "solutionId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| parentModelId | string | false |  | The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with solutionId. |
| solutionId | string | true |  | The ID of the solution to be cloned. |

## EureqaModelDetailResponse

```
{
  "properties": {
    "complexity": {
      "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
      "type": "integer"
    },
    "error": {
      "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
      "type": [
        "number",
        "null"
      ]
    },
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "eureqaSolutionId": {
      "description": "The ID of the solution.",
      "type": "string"
    },
    "expression": {
      "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
      "type": "string"
    },
    "expressionAnnotated": {
      "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
      "type": "string"
    },
    "plotData": {
      "description": "The plot data.",
      "items": {
        "properties": {
          "actual": {
            "description": "The actual value of the target variable for the specified row.",
            "type": "number"
          },
          "predicted": {
            "description": "The predicted value of the target by the solution for the specified row.",
            "type": "number"
          },
          "row": {
            "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.",
            "type": "integer"
          }
        },
        "required": [
          "actual",
          "predicted",
          "row"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "complexity",
    "error",
    "errorMetric",
    "eureqaSolutionId",
    "expression",
    "expressionAnnotated",
    "plotData"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| complexity | integer | true |  | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. |
| error | number,null | true |  | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. |
| errorMetric | string | true |  | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. |
| eureqaSolutionId | string | true |  | The ID of the solution. |
| expression | string | true |  | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. |
| expressionAnnotated | string | true |  | The expression, rendered with additional tags to assist in automatic parsing. |
| plotData | [PlotDataResponse] | true |  | The plot data. |

## Float

```
{
  "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).",
  "properties": {
    "max": {
      "description": "Maximum value for the parameter.",
      "type": "number"
    },
    "min": {
      "description": "Minimum value for the parameter.",
      "type": "number"
    },
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    }
  },
  "required": [
    "max",
    "min",
    "supportsGridSearch"
  ],
  "type": "object"
}
```

Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| max | number | true |  | Maximum value for the parameter. |
| min | number | true |  | Minimum value for the parameter. |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |

## FloatList

```
{
  "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).",
  "properties": {
    "maxLength": {
      "description": "Maximum permitted length of the list.",
      "minimum": 0,
      "type": "integer"
    },
    "maxVal": {
      "description": "Maximum permitted value.",
      "type": "number"
    },
    "minLength": {
      "description": "Minimum permitted length of the list.",
      "minimum": 0,
      "type": "integer"
    },
    "minVal": {
      "description": "Minimum permitted value.",
      "type": "number"
    },
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    }
  },
  "required": [
    "maxLength",
    "maxVal",
    "minLength",
    "minVal",
    "supportsGridSearch"
  ],
  "type": "object"
}
```

Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| maxLength | integer | true | minimum: 0 | Maximum permitted length of the list. |
| maxVal | number | true |  | Maximum permitted value. |
| minLength | integer | true | minimum: 0 | Minimum permitted length of the list. |
| minVal | number | true |  | Minimum permitted value. |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |

## FrozenModelCreate

```
{
  "properties": {
    "modelId": {
      "description": "The ID of an existing model to use as a source of training parameters.",
      "type": "string"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0.0 and 100.0.",
      "type": "number"
    },
    "trainingRowCount": {
      "description": "The integer number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.",
      "type": "integer"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string | true |  | The ID of an existing model to use as a source of training parameters. |
| nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| samplePct | number | false |  | The percentage of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. The specified percentage should be between 0.0 and 100.0. |
| trainingRowCount | integer | false |  | The integer number of rows of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. |

## FrozenModelListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "An array of the frozen models in a project.",
      "items": {
        "properties": {
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "samplePct",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [ModelDetailsResponse] | true |  | An array of the frozen models in a project. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |
| totalCount | integer | false |  | The total number of items across all pages. |

## GridSearchArgument

```
{
  "description": "The details of the grid search configuration.",
  "properties": {
    "algorithm": {
      "default": "Pattern Search",
      "description": "The algorithm to apply when running the grid search.",
      "enum": [
        "Pattern Search",
        "Accelerated Search",
        "Exhaustive Search",
        "Greedy Exhaustive Search",
        "Bayesian TPE Search",
        "Bayesian GP Search"
      ],
      "type": "string"
    },
    "batchSize": {
      "default": 2,
      "description": "The number of iterations to perform in each batch.",
      "maximum": 8,
      "minimum": 1,
      "type": "integer"
    },
    "maxIterations": {
      "default": 100,
      "description": "Sets the maximum number of iterations to perform.",
      "maximum": 10000,
      "minimum": 1,
      "type": "integer"
    },
    "randomState": {
      "default": 42,
      "description": "The random state/seed used for the grid search.",
      "minimum": 0,
      "type": "integer"
    },
    "searchType": {
      "default": "smart",
      "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.",
      "enum": [
        "full",
        "smart",
        "bayesian"
      ],
      "type": "string"
    },
    "wallClockTimeLimit": {
      "default": 300,
      "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.",
      "maximum": 604800,
      "minimum": 1,
      "type": "integer"
    }
  },
  "type": "object",
  "x-versionadded": "v2.38"
}
```

The details of the grid search configuration.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| algorithm | string | false |  | The algorithm to apply when running the grid search. |
| batchSize | integer | false | maximum: 8minimum: 1 | The number of iterations to perform in each batch. |
| maxIterations | integer | false | maximum: 10000minimum: 1 | Sets the maximum number of iterations to perform. |
| randomState | integer | false | minimum: 0 | The random state/seed used for the grid search. |
| searchType | string | false |  | The type of grid search to be performed. If not specified, DataRobot performs Smart Search. |
| wallClockTimeLimit | integer | false | maximum: 604800minimum: 1 | The wall clock time limit, in seconds. The model with the best score, at this point, is selected. |

### Enumerated Values

| Property | Value |
| --- | --- |
| algorithm | [Pattern Search, Accelerated Search, Exhaustive Search, Greedy Exhaustive Search, Bayesian TPE Search, Bayesian GP Search] |
| searchType | [full, smart, bayesian] |

## GridSearchScoresRetrieveResponse

```
{
  "properties": {
    "id": {
      "description": "The grid search scores metadata ID.",
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the model that scores belong to.",
      "type": "string"
    },
    "parameters": {
      "description": "The list of grid search parameters.",
      "items": {
        "items": {
          "properties": {
            "parameterName": {
              "description": "The name of the parameter.",
              "type": "string"
            },
            "parameterValue": {
              "description": "The value of the parameter.",
              "type": "number"
            }
          },
          "required": [
            "parameterName",
            "parameterValue"
          ],
          "type": "object",
          "x-versionadded": "v2.39"
        },
        "type": "array"
      },
      "maxItems": 100000,
      "minItems": 1,
      "type": "array"
    },
    "projectId": {
      "description": "The project the model belongs to.",
      "type": "string"
    },
    "scores": {
      "description": "The list of grid search scores.",
      "items": {
        "type": "number"
      },
      "maxItems": 100000,
      "minItems": 1,
      "type": "array"
    },
    "source": {
      "description": "The source for the scores. If omitted, the validation partition is used.",
      "enum": [
        "validation"
      ],
      "type": "string"
    }
  },
  "required": [
    "id",
    "modelId",
    "parameters",
    "projectId",
    "scores",
    "source"
  ],
  "type": "object",
  "x-versionadded": "v2.39"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| id | string | true |  | The grid search scores metadata ID. |
| modelId | string | true |  | The ID of the model that scores belong to. |
| parameters | [array] | true | maxItems: 100000minItems: 1 | The list of grid search parameters. |
| projectId | string | true |  | The project the model belongs to. |
| scores | [number] | true | maxItems: 100000minItems: 1 | The list of grid search scores. |
| source | string | true |  | The source for the scores. If omitted, the validation partition is used. |

### Enumerated Values

| Property | Value |
| --- | --- |
| source | validation |

## HyperparametersResponse

```
{
  "properties": {
    "buildingBlocks": {
      "description": "Mathematical operators and other components that comprise Eureqa Expressions.",
      "type": [
        "object",
        "null"
      ]
    },
    "errorMetric": {
      "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ",
      "type": [
        "string",
        "null"
      ]
    },
    "maxGenerations": {
      "description": "The maximum number of evolutionary generations to run.",
      "minimum": 32,
      "type": [
        "integer",
        "null"
      ]
    },
    "numThreads": {
      "description": "The number of threads Eureqa will run with.",
      "minimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "priorSolutions": {
      "description": "Prior Eureqa Solutions.",
      "items": {
        "description": "Prior solution.",
        "type": "string"
      },
      "type": "array"
    },
    "randomSeed": {
      "description": "Constant to seed Eureqa's pseudo-random number generator.",
      "minimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "splitMode": {
      "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.",
      "enum": [
        "custom",
        "1",
        "2"
      ],
      "type": [
        "string",
        "null"
      ]
    },
    "syncMigrations": {
      "description": "Whether Eureqa's migrations are synchronized.",
      "type": [
        "boolean",
        "null"
      ]
    },
    "targetExpressionFormat": {
      "description": "Constrain the target expression to the specified format.",
      "enum": [
        "None",
        "exponential",
        "featureInteraction"
      ],
      "type": [
        "string",
        "null"
      ]
    },
    "targetExpressionString": {
      "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.",
      "type": [
        "string",
        "null"
      ]
    },
    "timeoutSec": {
      "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.",
      "minimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "trainingFraction": {
      "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.",
      "maximum": 1,
      "minimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "trainingSplitExpr": {
      "description": "Valid Eureqa Expression to do Eureqa internal training splits.",
      "type": [
        "string",
        "null"
      ]
    },
    "validationFraction": {
      "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.",
      "maximum": 1,
      "minimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "validationSplitExpr": {
      "description": "Valid Eureqa Expression to do Eureqa internal validation splits.",
      "type": [
        "string",
        "null"
      ]
    },
    "weightExpr": {
      "description": "Eureqa Weight Expression.",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "buildingBlocks",
    "maxGenerations",
    "numThreads",
    "priorSolutions",
    "randomSeed",
    "splitMode",
    "syncMigrations",
    "targetExpressionFormat",
    "targetExpressionString",
    "timeoutSec",
    "trainingFraction",
    "trainingSplitExpr",
    "validationFraction",
    "validationSplitExpr",
    "weightExpr"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| buildingBlocks | object,null | true |  | Mathematical operators and other components that comprise Eureqa Expressions. |
| errorMetric | string,null | false |  | Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. |
| maxGenerations | integer,null | true | minimum: 32 | The maximum number of evolutionary generations to run. |
| numThreads | integer,null | true | minimum: 0 | The number of threads Eureqa will run with. |
| priorSolutions | [string] | true |  | Prior Eureqa Solutions. |
| randomSeed | integer,null | true | minimum: 0 | Constant to seed Eureqa's pseudo-random number generator. |
| splitMode | string,null | true |  | Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon. |
| syncMigrations | boolean,null | true |  | Whether Eureqa's migrations are synchronized. |
| targetExpressionFormat | string,null | true |  | Constrain the target expression to the specified format. |
| targetExpressionString | string,null | true |  | Eureqa Expression to constrain the form of the models that Eureqa will consider. |
| timeoutSec | number,null | true | minimum: 0 | The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached. |
| trainingFraction | number,null | true | maximum: 1minimum: 0 | The fraction of the DataRobot training data to use for Eureqa evolutionary training. |
| trainingSplitExpr | string,null | true |  | Valid Eureqa Expression to do Eureqa internal training splits. |
| validationFraction | number,null | true | maximum: 1minimum: 0 | The fraction of the DataRobot training data to use for Eureqa evolutionary validation. |
| validationSplitExpr | string,null | true |  | Valid Eureqa Expression to do Eureqa internal validation splits. |
| weightExpr | string,null | true |  | Eureqa Weight Expression. |

### Enumerated Values

| Property | Value |
| --- | --- |
| splitMode | [custom, 1, 2] |
| targetExpressionFormat | [None, exponential, featureInteraction] |

## Int

```
{
  "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.",
  "properties": {
    "max": {
      "description": "Maximum value for the parameter.",
      "type": "integer"
    },
    "min": {
      "description": "Minimum value for the parameter.",
      "type": "integer"
    },
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    }
  },
  "required": [
    "max",
    "min",
    "supportsGridSearch"
  ],
  "type": "object"
}
```

Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| max | integer | true |  | Maximum value for the parameter. |
| min | integer | true |  | Minimum value for the parameter. |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |

## IntList

```
{
  "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.",
  "properties": {
    "maxLength": {
      "description": "Maximum permitted length of the list.",
      "minimum": 0,
      "type": "integer"
    },
    "maxVal": {
      "description": "Maximum permitted value.",
      "type": "integer"
    },
    "minLength": {
      "description": "Minimum permitted length of the list.",
      "minimum": 0,
      "type": "integer"
    },
    "minVal": {
      "description": "Minimum permitted value.",
      "type": "integer"
    },
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    }
  },
  "required": [
    "maxLength",
    "maxVal",
    "minLength",
    "minVal",
    "supportsGridSearch"
  ],
  "type": "object"
}
```

Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| maxLength | integer | true | minimum: 0 | Maximum permitted length of the list. |
| maxVal | integer | true |  | Maximum permitted value. |
| minLength | integer | true | minimum: 0 | Minimum permitted length of the list. |
| minVal | integer | true |  | Minimum permitted value. |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |

## MessagesInfo

```
{
  "properties": {
    "messages": {
      "description": "List of data quality messages. The list may include reports on more than one data quality issue, if present.",
      "items": {
        "properties": {
          "additionalInfo": {
            "description": "Zero or more text strings for secondary display after user clicks for more information.",
            "items": {
              "type": "string"
            },
            "maxItems": 50,
            "type": "array"
          },
          "messageLevel": {
            "description": "Message severity level.",
            "enum": [
              "CRITICAL",
              "INFORMATIONAL",
              "NO_ISSUES",
              "WARNING"
            ],
            "type": "string"
          },
          "messageText": {
            "description": "Text for primary display in UI.",
            "maxLength": 500,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "messageLevel",
          "messageText"
        ],
        "type": "object"
      },
      "maxItems": 50,
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "messages"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| messages | [OneMessageInfo] | true | maxItems: 50minItems: 1 | List of data quality messages. The list may include reports on more than one data quality issue, if present. |

## MissingReportRetrieve

```
{
  "properties": {
    "missingValuesReport": {
      "description": "Missing values report, which contains an array of reports for individual features.",
      "items": {
        "properties": {
          "feature": {
            "description": "The name of the feature.",
            "type": "string"
          },
          "missingCount": {
            "description": "The number of missing values in the training data.",
            "type": "integer"
          },
          "missingPercentage": {
            "description": "The percentage of missing values in the training data.",
            "maximum": 1,
            "minimum": 0,
            "type": "number"
          },
          "tasks": {
            "additionalProperties": {
              "properties": {
                "descriptions": {
                  "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.",
                  "items": {
                    "type": "string"
                  },
                  "type": "array"
                },
                "name": {
                  "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.",
                  "type": "string"
                }
              },
              "required": [
                "descriptions",
                "name"
              ],
              "type": "object"
            },
            "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.",
            "type": "object"
          },
          "type": {
            "description": "The type of the feature, e.g., `Categorical` or `Numeric`.",
            "type": "string"
          }
        },
        "required": [
          "feature",
          "missingCount",
          "missingPercentage",
          "tasks",
          "type"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "missingValuesReport"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| missingValuesReport | [PerFeatureMissingReport] | true |  | Missing values report, which contains an array of reports for individual features. |

## ModelAdvancedTuning

```
{
  "properties": {
    "gridSearchArguments": {
      "description": "The details of the grid search configuration.",
      "properties": {
        "algorithm": {
          "default": "Pattern Search",
          "description": "The algorithm to apply when running the grid search.",
          "enum": [
            "Pattern Search",
            "Accelerated Search",
            "Exhaustive Search",
            "Greedy Exhaustive Search",
            "Bayesian TPE Search",
            "Bayesian GP Search"
          ],
          "type": "string"
        },
        "batchSize": {
          "default": 2,
          "description": "The number of iterations to perform in each batch.",
          "maximum": 8,
          "minimum": 1,
          "type": "integer"
        },
        "maxIterations": {
          "default": 100,
          "description": "Sets the maximum number of iterations to perform.",
          "maximum": 10000,
          "minimum": 1,
          "type": "integer"
        },
        "randomState": {
          "default": 42,
          "description": "The random state/seed used for the grid search.",
          "minimum": 0,
          "type": "integer"
        },
        "searchType": {
          "default": "smart",
          "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.",
          "enum": [
            "full",
            "smart",
            "bayesian"
          ],
          "type": "string"
        },
        "wallClockTimeLimit": {
          "default": 300,
          "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.",
          "maximum": 604800,
          "minimum": 1,
          "type": "integer"
        }
      },
      "type": "object",
      "x-versionadded": "v2.38"
    },
    "tuningDescription": {
      "description": "Human-readable description of this advanced-tuning request.",
      "type": "string"
    },
    "tuningParameters": {
      "description": "Parameters to tune.",
      "items": {
        "properties": {
          "parameterId": {
            "description": "ID of the parameter whose value to set.",
            "type": "string"
          },
          "value": {
            "description": "Value for the specified parameter.",
            "oneOf": [
              {
                "anyOf": [
                  {
                    "type": "string"
                  },
                  {
                    "type": "integer"
                  },
                  {
                    "type": "boolean"
                  },
                  {
                    "type": "number"
                  },
                  {
                    "type": "null"
                  }
                ]
              },
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "string"
                    },
                    {
                      "type": "integer"
                    },
                    {
                      "type": "boolean"
                    },
                    {
                      "type": "number"
                    },
                    {
                      "type": "null"
                    }
                  ]
                },
                "type": "array"
              }
            ]
          }
        },
        "required": [
          "parameterId",
          "value"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "tuningParameters"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| gridSearchArguments | GridSearchArgument | false |  | The details of the grid search configuration. |
| tuningDescription | string | false |  | Human-readable description of this advanced-tuning request. |
| tuningParameters | [TuningParameter] | true |  | Parameters to tune. |

## ModelCapabilitiesRetrieveResponse

```
{
  "properties": {
    "eligibleForPrime": {
      "description": "`True` if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible.",
      "type": "boolean",
      "x-versiondeprecated": "v2.35"
    },
    "hasParameters": {
      "description": "`True` if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters.",
      "type": "boolean"
    },
    "hasWordCloud": {
      "description": "True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud.",
      "type": "boolean"
    },
    "reasons": {
      "description": "Information on why the capability is unsupported for the model.",
      "properties": {
        "supportsAccuracyOverTime": {
          "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsAnomalyAssessment": {
          "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsAnomalyOverTime": {
          "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsClusterInsights": {
          "description": "If present, the reason why Cluster Insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsConfusionMatrix": {
          "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.33"
        },
        "supportsDocumentTextExtractionSampleInsight": {
          "description": "If present, the reason document text extraction sample insights are not supported for the model.",
          "type": "string",
          "x-versionadded": "v2.29"
        },
        "supportsForecastAccuracy": {
          "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsForecastVsActual": {
          "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsImageActivationMaps": {
          "description": "If present, the reason image activation maps are not supported for the model.",
          "type": "string"
        },
        "supportsImageEmbedding": {
          "description": "If present, the reason image embeddings are not supported for the model.",
          "type": "string"
        },
        "supportsLiftChart": {
          "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.31"
        },
        "supportsPeriodAccuracy": {
          "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsPredictionExplanations": {
          "description": "If present, the reason why Prediction Explanations cannot be computed for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsPredictionIntervals": {
          "description": "If present, the reason why Prediction Intervals cannot be computed for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsResiduals": {
          "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.30"
        },
        "supportsRocCurve": {
          "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.",
          "type": "string",
          "x-versionadded": "v2.32"
        },
        "supportsSeriesInsights": {
          "description": "If present, the reason why Series Insights cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        },
        "supportsStability": {
          "description": "If present, the reason why Stability plots cannot be generated for the model.",
          "type": "string",
          "x-versionadded": "v2.34"
        }
      },
      "type": "object"
    },
    "supportsAccuracyOverTime": {
      "description": "`True` if Accuracy Over Time plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsAdvancedTuning": {
      "description": "`True` if model supports Advanced Tuning.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "supportsAnomalyAssessment": {
      "description": "`True` if Anomaly Assessment insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsAnomalyOverTime": {
      "description": "`True` if Anomaly Over Time plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsBlending": {
      "description": "`True` if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations.",
      "type": "boolean"
    },
    "supportsClusterInsights": {
      "description": "`True` if Cluster Insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsCodeGeneration": {
      "description": "`True` if the model supports export of model's source code or compiled Java executable.",
      "type": "boolean",
      "x-versionadded": "v2.18"
    },
    "supportsCoefficients": {
      "description": "`True` if model coefficients are available.",
      "type": "boolean",
      "x-versionadded": "v2.32"
    },
    "supportsConfusionMatrix": {
      "description": "`True` if Confusion Matrix can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "supportsDocumentTextExtractionSampleInsight": {
      "description": "`True` if the model has document column(s) and document text extraction samples can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "supportsEarlyStopping": {
      "description": "`True` if this is an early stopping tree-based model and number of trained iterations can be retrieved.",
      "type": "boolean",
      "x-versionadded": "v2.22"
    },
    "supportsForecastAccuracy": {
      "description": "`True` if Forecast Accuracy plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsForecastVsActual": {
      "description": "`True` if Forecast vs Actual plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsImageActivationMaps": {
      "description": "`True` if the model has image column(s) and activation maps can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsImageEmbedding": {
      "description": "`True` if the model has image column(s) and image embeddings can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsLiftChart": {
      "description": "`True` if Lift Chart can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.31"
    },
    "supportsModelPackageExport": {
      "description": "`True` if the model can be exported as a model package.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsModelTrainingMetrics": {
      "description": "When `True` , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion.",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "supportsMonotonicConstraints": {
      "description": "`True` if the model supports monotonic constraints.",
      "type": "boolean"
    },
    "supportsNNVisualizations": {
      "description": "`True` if the model supports neuralNetworkVisualizations.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    },
    "supportsPerLabelMetrics": {
      "description": "`True` if the experiment qualifies as a multilabel project.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "supportsPeriodAccuracy": {
      "description": "`True` if Period Accuracy insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionExplanations": {
      "description": "`True` if the model supports Prediction Explanations.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionIntervals": {
      "description": "`True` if Prediction Intervals can be computed for predictions generated by this model.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsResiduals": {
      "description": "When `True`, the model supports residuals and residuals data can be retrieved.",
      "type": "boolean",
      "x-versionadded": "v2.30"
    },
    "supportsRocCurve": {
      "description": "`True` if ROC Curve can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.32"
    },
    "supportsSeriesInsights": {
      "description": "`True` if Series Insights can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    },
    "supportsShap": {
      "description": "`True` if the model supports Shapley package, i.e., Shapley-based feature importance.",
      "type": "boolean",
      "x-versionadded": "v2.18"
    },
    "supportsStability": {
      "description": "`True` if Stability plots can be generated.",
      "type": "boolean",
      "x-versionadded": "v2.34"
    }
  },
  "required": [
    "eligibleForPrime",
    "hasParameters",
    "hasWordCloud",
    "supportsAccuracyOverTime",
    "supportsAdvancedTuning",
    "supportsAnomalyAssessment",
    "supportsAnomalyOverTime",
    "supportsBlending",
    "supportsClusterInsights",
    "supportsCodeGeneration",
    "supportsCoefficients",
    "supportsConfusionMatrix",
    "supportsDocumentTextExtractionSampleInsight",
    "supportsForecastAccuracy",
    "supportsForecastVsActual",
    "supportsImageActivationMaps",
    "supportsImageEmbedding",
    "supportsLiftChart",
    "supportsModelTrainingMetrics",
    "supportsMonotonicConstraints",
    "supportsNNVisualizations",
    "supportsPerLabelMetrics",
    "supportsPredictionExplanations",
    "supportsPredictionIntervals",
    "supportsResiduals",
    "supportsRocCurve",
    "supportsSeriesInsights",
    "supportsShap",
    "supportsStability"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| eligibleForPrime | boolean | true |  | True if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible. |
| hasParameters | boolean | true |  | True if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters. |
| hasWordCloud | boolean | true |  | True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud. |
| reasons | Reasons | false |  | Information on why the capability is unsupported for the model. |
| supportsAccuracyOverTime | boolean | true |  | True if Accuracy Over Time plots can be generated. |
| supportsAdvancedTuning | boolean | true |  | True if model supports Advanced Tuning. |
| supportsAnomalyAssessment | boolean | true |  | True if Anomaly Assessment insights can be generated. |
| supportsAnomalyOverTime | boolean | true |  | True if Anomaly Over Time plots can be generated. |
| supportsBlending | boolean | true |  | True if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations. |
| supportsClusterInsights | boolean | true |  | True if Cluster Insights can be generated. |
| supportsCodeGeneration | boolean | true |  | True if the model supports export of model's source code or compiled Java executable. |
| supportsCoefficients | boolean | true |  | True if model coefficients are available. |
| supportsConfusionMatrix | boolean | true |  | True if Confusion Matrix can be generated. |
| supportsDocumentTextExtractionSampleInsight | boolean | true |  | True if the model has document column(s) and document text extraction samples can be generated. |
| supportsEarlyStopping | boolean | false |  | True if this is an early stopping tree-based model and number of trained iterations can be retrieved. |
| supportsForecastAccuracy | boolean | true |  | True if Forecast Accuracy plots can be generated. |
| supportsForecastVsActual | boolean | true |  | True if Forecast vs Actual plots can be generated. |
| supportsImageActivationMaps | boolean | true |  | True if the model has image column(s) and activation maps can be generated. |
| supportsImageEmbedding | boolean | true |  | True if the model has image column(s) and image embeddings can be generated. |
| supportsLiftChart | boolean | true |  | True if Lift Chart can be generated. |
| supportsModelPackageExport | boolean | false |  | True if the model can be exported as a model package. |
| supportsModelTrainingMetrics | boolean | true |  | When True , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion. |
| supportsMonotonicConstraints | boolean | true |  | True if the model supports monotonic constraints. |
| supportsNNVisualizations | boolean | true |  | True if the model supports neuralNetworkVisualizations. |
| supportsPerLabelMetrics | boolean | true |  | True if the experiment qualifies as a multilabel project. |
| supportsPeriodAccuracy | boolean | false |  | True if Period Accuracy insights can be generated. |
| supportsPredictionExplanations | boolean | true |  | True if the model supports Prediction Explanations. |
| supportsPredictionIntervals | boolean | true |  | True if Prediction Intervals can be computed for predictions generated by this model. |
| supportsResiduals | boolean | true |  | When True, the model supports residuals and residuals data can be retrieved. |
| supportsRocCurve | boolean | true |  | True if ROC Curve can be generated. |
| supportsSeriesInsights | boolean | true |  | True if Series Insights can be generated. |
| supportsShap | boolean | true |  | True if the model supports Shapley package, i.e., Shapley-based feature importance. |
| supportsStability | boolean | true |  | True if Stability plots can be generated. |

## ModelDetailsResponse

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "samplePct",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| hasFinetuners | boolean | false |  | Whether a model has fine tuners. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isAugmented | boolean | false |  | Whether a model was trained using augmentation. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isNClustersDynamicallyDetermined | boolean | false |  | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| lifecycle | ModelLifecycle | true |  | Object returning model lifecycle. |
| linkFunction | string,null | true |  | The link function the final modeler uses in the blueprint. If no link function exists, returns null. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The family the model belongs to, e.g., SVM, GBM, etc. |
| modelFamilyFullName | string | true |  | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| monotonicDecreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer,null | false |  | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. |
| predictionThresholdReadOnly | boolean | true |  | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| supportsComposableMl | boolean | true |  | indicates whether this model is supported in Composable ML. |
| supportsMonotonicConstraints | boolean | true |  | whether this model supports enforcing monotonic constraints |
| timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplingMethod | [random, latest] |

## ModelFeatureListResponse

```
{
  "properties": {
    "aPrioriFeatureNames": {
      "description": "(Deprecated in version v2.11) Renamed to `knownInAdvanceFeatureNames`. This parameter always has the same value as `knownInAdvanceFeatureNames` and will be removed in a future release.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "featureNames": {
      "description": "An array of the names of all features used by the specified model.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "knownInAdvanceFeatureNames": {
      "description": "An array of the names of time series known-in-advance features used by the specified model.",
      "items": {
        "type": "string"
      },
      "type": "array"
    }
  },
  "required": [
    "aPrioriFeatureNames",
    "featureNames",
    "knownInAdvanceFeatureNames"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| aPrioriFeatureNames | [string] | true |  | (Deprecated in version v2.11) Renamed to knownInAdvanceFeatureNames. This parameter always has the same value as knownInAdvanceFeatureNames and will be removed in a future release. |
| featureNames | [string] | true |  | An array of the names of all features used by the specified model. |
| knownInAdvanceFeatureNames | [string] | true |  | An array of the names of time series known-in-advance features used by the specified model. |

## ModelJobResponse

```
{
  "properties": {
    "blueprintId": {
      "description": "The blueprint used by the model - note that this is not an ObjectId.",
      "type": "string"
    },
    "featurelistId": {
      "description": "The ID of the featurelist the model is using.",
      "type": "string"
    },
    "id": {
      "description": "The job ID.",
      "type": "string"
    },
    "isBlocked": {
      "description": "True if a job is waiting for its dependencies to be resolved first.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the job was trained using GPU capabilities.",
      "type": "boolean"
    },
    "modelCategory": {
      "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
      "enum": [
        "model",
        "prime",
        "blend"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "modelType": {
      "description": "The type of model used by the job.",
      "type": "string"
    },
    "processes": {
      "description": "The list of processes the modeling job includes.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "projectId": {
      "description": "The project the job belongs to.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset the job is using.",
      "type": "number"
    },
    "status": {
      "description": "The status of the job.",
      "enum": [
        "queue",
        "inprogress",
        "error",
        "ABORTED",
        "COMPLETED"
      ],
      "type": "string"
    }
  },
  "required": [
    "blueprintId",
    "featurelistId",
    "id",
    "isBlocked",
    "modelCategory",
    "modelId",
    "modelType",
    "processes",
    "projectId",
    "status"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blueprintId | string | true |  | The blueprint used by the model - note that this is not an ObjectId. |
| featurelistId | string | true |  | The ID of the featurelist the model is using. |
| id | string | true |  | The job ID. |
| isBlocked | boolean | true |  | True if a job is waiting for its dependencies to be resolved first. |
| isTrainedOnGpu | boolean | false |  | Whether the job was trained using GPU capabilities. |
| modelCategory | string | true |  | Indicates what kind of model this is. Will be combined for combined models. |
| modelId | string | true |  | The ID of the model. |
| modelType | string | true |  | The type of model used by the job. |
| processes | [string] | true |  | The list of processes the modeling job includes. |
| projectId | string | true |  | The project the job belongs to. |
| samplePct | number | false |  | The percentage of the dataset the job is using. |
| status | string | true |  | The status of the job. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | [model, prime, blend] |
| status | [queue, inprogress, error, ABORTED, COMPLETED] |

## ModelLifecycle

```
{
  "description": "Object returning model lifecycle.",
  "properties": {
    "reason": {
      "description": "The reason for the lifecycle stage. None if the model is active.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.30"
    },
    "stage": {
      "description": "The model lifecycle stage.",
      "enum": [
        "active",
        "deprecated",
        "disabled"
      ],
      "type": "string",
      "x-versionadded": "v2.30"
    }
  },
  "required": [
    "reason",
    "stage"
  ],
  "type": "object"
}
```

Object returning model lifecycle.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| reason | string,null | true |  | The reason for the lifecycle stage. None if the model is active. |
| stage | string | true |  | The model lifecycle stage. |

### Enumerated Values

| Property | Value |
| --- | --- |
| stage | [active, deprecated, disabled] |

## ModelParametersRetrieveResponse

```
{
  "properties": {
    "derivedFeatures": {
      "description": "An array of preprocessing information about derived features used in the model.",
      "items": {
        "properties": {
          "coefficient": {
            "description": "The coefficient for this feature.",
            "type": "number"
          },
          "derivedFeature": {
            "description": "The name of the derived feature.",
            "type": "string"
          },
          "originalFeature": {
            "description": "The name of the feature used to derive this feature.",
            "type": "string"
          },
          "stageCoefficients": {
            "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).",
            "items": {
              "properties": {
                "coefficient": {
                  "description": "The corresponding value of the coefficient for that stage.",
                  "type": "number"
                },
                "stage": {
                  "description": "The name of the stage.",
                  "type": "string"
                }
              },
              "required": [
                "coefficient",
                "stage"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "transformations": {
            "description": "An array of json objects describing the transformations applied to create this derived feature.",
            "items": {
              "properties": {
                "name": {
                  "description": "The name of the transformation.",
                  "type": "string"
                },
                "value": {
                  "description": "The value used in carrying it out.",
                  "type": "string"
                }
              },
              "required": [
                "name",
                "value"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "type": {
            "description": "The type of this feature.",
            "type": "string"
          }
        },
        "required": [
          "coefficient",
          "derivedFeature",
          "originalFeature",
          "stageCoefficients",
          "transformations",
          "type"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "parameters": {
      "description": "An array of parameters that are related to the whole model.",
      "items": {
        "properties": {
          "name": {
            "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".",
            "type": "string"
          },
          "value": {
            "description": "The value of the parameter.",
            "type": "string"
          }
        },
        "required": [
          "name",
          "value"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "derivedFeatures",
    "parameters"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| derivedFeatures | [DerivedFeatures] | true |  | An array of preprocessing information about derived features used in the model. |
| parameters | [Parameters] | true |  | An array of parameters that are related to the whole model. |

## ModelRecordResponse

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object",
      "x-versionadded": "v2.33"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelFamily": {
      "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.33"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelNumber",
    "modelType",
    "parentModelId",
    "processes",
    "projectId",
    "samplePct",
    "trainingRowCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |

## ModelRecordsResponse

```
{
  "properties": {
    "count": {
      "description": "The number of models returned on this page.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.33"
    },
    "data": {
      "description": "Model records.",
      "items": {
        "oneOf": [
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object",
                "x-versionadded": "v2.33"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.33"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingRowCount"
            ],
            "type": "object"
          },
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "dataSelectionMethod": {
                "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
                "enum": [
                  "duration",
                  "rowCount",
                  "selectedDateRange",
                  "useProjectSettings"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object",
                "x-versionadded": "v2.33"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.33"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "samplingMethod": {
                "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
                "enum": [
                  "random",
                  "latest"
                ],
                "type": "string",
                "x-versionadded": "v2.33"
              },
              "timeWindowSamplePct": {
                "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
                "exclusiveMaximum": 100,
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingDuration": {
                "description": "the duration spanned by the dates in the partition column for the data used to train the model",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingEndDate": {
                "description": "the end date of the dates in the partition column for the data used to train the model",
                "format": "date-time",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.33"
              },
              "trainingStartDate": {
                "description": "the start date of the dates in the partition column for the data used to train the model",
                "format": "date-time",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.33"
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingDuration",
              "trainingEndDate",
              "trainingRowCount",
              "trainingStartDate"
            ],
            "type": "object"
          },
          {
            "properties": {
              "blenderModels": {
                "description": "Models that are in the blender.",
                "items": {
                  "type": "integer"
                },
                "maxItems": 100,
                "type": "array",
                "x-versionadded": "v2.36"
              },
              "blueprintId": {
                "description": "The blueprint used to construct the model.",
                "type": "string"
              },
              "externalPredictionModel": {
                "description": "If the model is an external prediction model.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "featurelistId": {
                "description": "The ID of the feature list used by the model.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "featurelistName": {
                "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "frozenPct": {
                "description": "The training percent used to train the frozen model.",
                "type": [
                  "number",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "hasCodegen": {
                "description": "If the model has a codegen JAR file.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "icons": {
                "description": "The icons associated with the model.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.36"
              },
              "id": {
                "description": "The ID of the model.",
                "type": "string"
              },
              "isBlender": {
                "description": "If the model is a blender.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isCustom": {
                "description": "If the model contains custom tasks.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isFrozen": {
                "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
                "type": "boolean"
              },
              "isStarred": {
                "description": "Indicates whether the model has been starred.",
                "type": "boolean"
              },
              "isTrainedIntoHoldout": {
                "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
                "type": "boolean"
              },
              "isTrainedIntoValidation": {
                "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
                "type": "boolean"
              },
              "isTrainedOnGpu": {
                "description": "Whether the model was trained using GPU workers.",
                "type": "boolean",
                "x-versionadded": "v2.33"
              },
              "isTransparent": {
                "description": "If the model is a transparent model with exposed coefficients.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "isUserModel": {
                "description": "If the model was created with Composable ML.",
                "type": "boolean",
                "x-versionadded": "v2.36"
              },
              "metrics": {
                "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
                "type": "object"
              },
              "modelCategory": {
                "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
                "enum": [
                  "model",
                  "prime",
                  "blend",
                  "combined",
                  "incrementalLearning"
                ],
                "type": "string"
              },
              "modelFamily": {
                "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
                "type": "string"
              },
              "modelNumber": {
                "description": "The model number from the Leaderboard.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ]
              },
              "modelType": {
                "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
                "type": "string"
              },
              "numberOfClusters": {
                "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.",
                "type": [
                  "integer",
                  "null"
                ],
                "x-versionadded": "v2.34"
              },
              "parentModelId": {
                "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
                "type": [
                  "string",
                  "null"
                ]
              },
              "processes": {
                "description": "The list of processes used by the model.",
                "items": {
                  "type": "string"
                },
                "maxItems": 100,
                "type": "array"
              },
              "projectId": {
                "description": "The ID of the project to which the model belongs.",
                "type": "string"
              },
              "samplePct": {
                "description": "The percentage of the dataset used in training the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "number",
                  "null"
                ]
              },
              "trainingRowCount": {
                "description": "The number of rows used to train the model.",
                "exclusiveMinimum": 0,
                "type": [
                  "integer",
                  "null"
                ]
              }
            },
            "required": [
              "blenderModels",
              "blueprintId",
              "externalPredictionModel",
              "featurelistId",
              "featurelistName",
              "frozenPct",
              "hasCodegen",
              "icons",
              "id",
              "isBlender",
              "isCustom",
              "isFrozen",
              "isStarred",
              "isTrainedIntoHoldout",
              "isTrainedIntoValidation",
              "isTrainedOnGpu",
              "isTransparent",
              "isUserModel",
              "metrics",
              "modelCategory",
              "modelFamily",
              "modelNumber",
              "modelType",
              "numberOfClusters",
              "parentModelId",
              "processes",
              "projectId",
              "samplePct",
              "trainingRowCount"
            ],
            "type": "object",
            "x-versionadded": "v2.34"
          }
        ]
      },
      "type": "array",
      "x-versionadded": "v2.33"
    },
    "next": {
      "description": "A URL pointing to the next page (if `null`, there is no next page).",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "previous": {
      "description": "A URL pointing to the previous page (if `null`, there is no previous page).",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.33"
    },
    "totalCount": {
      "description": "Total number of models after filters applied.",
      "minimum": 0,
      "type": "integer",
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | true | minimum: 0 | The number of models returned on this page. |
| data | [oneOf] | true |  | Model records. |

oneOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | ModelRecordResponse | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | DatetimeModelRecordResponse | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | UnsupervisedClusteringModelRecordResponse | false |  | none |

continued

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| next | string,null | true |  | A URL pointing to the next page (if null, there is no next page). |
| previous | string,null | true |  | A URL pointing to the previous page (if null, there is no previous page). |
| totalCount | integer | true | minimum: 0 | Total number of models after filters applied. |

## ModelRetrainResponse

```
{
  "properties": {
    "message": {
      "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| message | string | true |  | any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. |

## ModelUpdate

```
{
  "properties": {
    "isStarred": {
      "description": "Mark model either as starred or unstarred.",
      "type": "boolean"
    },
    "predictionThreshold": {
      "description": "Threshold used for binary classification in predictions. Default value is 0.5.",
      "maximum": 1,
      "minimum": 0,
      "type": "number"
    }
  },
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| isStarred | boolean | false |  | Mark model either as starred or unstarred. |
| predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions. Default value is 0.5. |

## ModelingJobListResponse

```
{
  "properties": {
    "data": {
      "description": "List of modeling jobs.",
      "items": {
        "properties": {
          "blueprintId": {
            "description": "The blueprint used by the model - note that this is not an ObjectId.",
            "type": "string"
          },
          "featurelistId": {
            "description": "The ID of the featurelist the model is using.",
            "type": "string"
          },
          "id": {
            "description": "The job ID.",
            "type": "string"
          },
          "isBlocked": {
            "description": "True if a job is waiting for its dependencies to be resolved first.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the job was trained using GPU capabilities.",
            "type": "boolean"
          },
          "modelCategory": {
            "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.",
            "enum": [
              "model",
              "prime",
              "blend"
            ],
            "type": "string"
          },
          "modelId": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "modelType": {
            "description": "The type of model used by the job.",
            "type": "string"
          },
          "processes": {
            "description": "The list of processes the modeling job includes.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "projectId": {
            "description": "The project the job belongs to.",
            "type": "string"
          },
          "samplePct": {
            "description": "The percentage of the dataset the job is using.",
            "type": "number"
          },
          "status": {
            "description": "The status of the job.",
            "enum": [
              "queue",
              "inprogress",
              "error",
              "ABORTED",
              "COMPLETED"
            ],
            "type": "string"
          }
        },
        "required": [
          "blueprintId",
          "featurelistId",
          "id",
          "isBlocked",
          "modelCategory",
          "modelId",
          "modelType",
          "processes",
          "projectId",
          "status"
        ],
        "type": "object"
      },
      "type": "array"
    }
  },
  "required": [
    "data"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| data | [ModelJobResponse] | true |  | List of modeling jobs. |

## NumIterationsTrainedData

```
{
  "properties": {
    "numIterations": {
      "description": "The number of iterations run in this stage of modeling.",
      "minimum": 0,
      "type": "integer"
    },
    "stage": {
      "description": "Modeling stage or None if it is a single-stage model",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "numIterations",
    "stage"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| numIterations | integer | true | minimum: 0 | The number of iterations run in this stage of modeling. |
| stage | string,null | true |  | Modeling stage or None if it is a single-stage model |

## NumIterationsTrainedResponse

```
{
  "properties": {
    "data": {
      "description": "Number of estimators or iterations for a single model stage",
      "items": {
        "properties": {
          "numIterations": {
            "description": "The number of iterations run in this stage of modeling.",
            "minimum": 0,
            "type": "integer"
          },
          "stage": {
            "description": "Modeling stage or None if it is a single-stage model",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "numIterations",
          "stage"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "modelId": {
      "description": "The model ID.",
      "type": "string"
    },
    "projectId": {
      "description": "The project ID.",
      "type": "string"
    }
  },
  "required": [
    "data",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| data | [NumIterationsTrainedData] | true |  | Number of estimators or iterations for a single model stage |
| modelId | string | true |  | The model ID. |
| projectId | string | true |  | The project ID. |

## OneMessageInfo

```
{
  "properties": {
    "additionalInfo": {
      "description": "Zero or more text strings for secondary display after user clicks for more information.",
      "items": {
        "type": "string"
      },
      "maxItems": 50,
      "type": "array"
    },
    "messageLevel": {
      "description": "Message severity level.",
      "enum": [
        "CRITICAL",
        "INFORMATIONAL",
        "NO_ISSUES",
        "WARNING"
      ],
      "type": "string"
    },
    "messageText": {
      "description": "Text for primary display in UI.",
      "maxLength": 500,
      "minLength": 1,
      "type": "string"
    }
  },
  "required": [
    "messageLevel",
    "messageText"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| additionalInfo | [string] | false | maxItems: 50 | Zero or more text strings for secondary display after user clicks for more information. |
| messageLevel | string | true |  | Message severity level. |
| messageText | string | true | maxLength: 500minLength: 1minLength: 1 | Text for primary display in UI. |

### Enumerated Values

| Property | Value |
| --- | --- |
| messageLevel | [CRITICAL, INFORMATIONAL, NO_ISSUES, WARNING] |

## ParameterItem

```
{
  "properties": {
    "parameterName": {
      "description": "The name of the parameter.",
      "type": "string"
    },
    "parameterValue": {
      "description": "The value of the parameter.",
      "type": "number"
    }
  },
  "required": [
    "parameterName",
    "parameterValue"
  ],
  "type": "object",
  "x-versionadded": "v2.39"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| parameterName | string | true |  | The name of the parameter. |
| parameterValue | number | true |  | The value of the parameter. |

## Parameters

```
{
  "properties": {
    "name": {
      "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".",
      "type": "string"
    },
    "value": {
      "description": "The value of the parameter.",
      "type": "string"
    }
  },
  "required": [
    "name",
    "value"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| name | string | true |  | The name of the parameter identifying what it means for the model, e.g. "Intercept". |
| value | string | true |  | The value of the parameter. |

## ParetoFrontResponse

```
{
  "properties": {
    "errorMetric": {
      "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.",
      "type": "string"
    },
    "hyperparameters": {
      "description": "The hyperparameters used by this run of the Eureqa blueprint.",
      "oneOf": [
        {
          "properties": {
            "buildingBlocks": {
              "description": "Mathematical operators and other components that comprise Eureqa Expressions.",
              "type": [
                "object",
                "null"
              ]
            },
            "errorMetric": {
              "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ",
              "type": [
                "string",
                "null"
              ]
            },
            "maxGenerations": {
              "description": "The maximum number of evolutionary generations to run.",
              "minimum": 32,
              "type": [
                "integer",
                "null"
              ]
            },
            "numThreads": {
              "description": "The number of threads Eureqa will run with.",
              "minimum": 0,
              "type": [
                "integer",
                "null"
              ]
            },
            "priorSolutions": {
              "description": "Prior Eureqa Solutions.",
              "items": {
                "description": "Prior solution.",
                "type": "string"
              },
              "type": "array"
            },
            "randomSeed": {
              "description": "Constant to seed Eureqa's pseudo-random number generator.",
              "minimum": 0,
              "type": [
                "integer",
                "null"
              ]
            },
            "splitMode": {
              "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.",
              "enum": [
                "custom",
                "1",
                "2"
              ],
              "type": [
                "string",
                "null"
              ]
            },
            "syncMigrations": {
              "description": "Whether Eureqa's migrations are synchronized.",
              "type": [
                "boolean",
                "null"
              ]
            },
            "targetExpressionFormat": {
              "description": "Constrain the target expression to the specified format.",
              "enum": [
                "None",
                "exponential",
                "featureInteraction"
              ],
              "type": [
                "string",
                "null"
              ]
            },
            "targetExpressionString": {
              "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.",
              "type": [
                "string",
                "null"
              ]
            },
            "timeoutSec": {
              "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.",
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "trainingFraction": {
              "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.",
              "maximum": 1,
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "trainingSplitExpr": {
              "description": "Valid Eureqa Expression to do Eureqa internal training splits.",
              "type": [
                "string",
                "null"
              ]
            },
            "validationFraction": {
              "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.",
              "maximum": 1,
              "minimum": 0,
              "type": [
                "number",
                "null"
              ]
            },
            "validationSplitExpr": {
              "description": "Valid Eureqa Expression to do Eureqa internal validation splits.",
              "type": [
                "string",
                "null"
              ]
            },
            "weightExpr": {
              "description": "Eureqa Weight Expression.",
              "type": [
                "string",
                "null"
              ]
            }
          },
          "required": [
            "buildingBlocks",
            "maxGenerations",
            "numThreads",
            "priorSolutions",
            "randomSeed",
            "splitMode",
            "syncMigrations",
            "targetExpressionFormat",
            "targetExpressionString",
            "timeoutSec",
            "trainingFraction",
            "trainingSplitExpr",
            "validationFraction",
            "validationSplitExpr",
            "weightExpr"
          ],
          "type": "object"
        },
        {
          "type": "null"
        }
      ]
    },
    "projectId": {
      "description": "The project ID of the Eureqa model.",
      "type": "string"
    },
    "solutions": {
      "description": "The Eureqa model solutions.",
      "items": {
        "properties": {
          "bestModel": {
            "description": "True if this solution generates the best model.",
            "type": "boolean"
          },
          "complexity": {
            "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
            "type": "integer"
          },
          "error": {
            "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
            "type": [
              "number",
              "null"
            ]
          },
          "eureqaSolutionId": {
            "description": "The ID of the solution.",
            "type": "string"
          },
          "expression": {
            "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
            "type": "string"
          },
          "expressionAnnotated": {
            "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
            "type": "string"
          }
        },
        "required": [
          "bestModel",
          "complexity",
          "error",
          "eureqaSolutionId",
          "expression",
          "expressionAnnotated"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "targetType": {
      "description": "The type of the target variable.",
      "enum": [
        "Regression",
        "Binary"
      ],
      "type": "string"
    }
  },
  "required": [
    "errorMetric",
    "hyperparameters",
    "projectId",
    "solutions",
    "targetType"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| errorMetric | string | true |  | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. |
| hyperparameters | any | true |  | The hyperparameters used by this run of the Eureqa blueprint. |

oneOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | HyperparametersResponse | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | null | false |  | none |

continued

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| projectId | string | true |  | The project ID of the Eureqa model. |
| solutions | [SolutionResponse] | true |  | The Eureqa model solutions. |
| targetType | string | true |  | The type of the target variable. |

### Enumerated Values

| Property | Value |
| --- | --- |
| targetType | [Regression, Binary] |

## PerFeatureMissingReport

```
{
  "properties": {
    "feature": {
      "description": "The name of the feature.",
      "type": "string"
    },
    "missingCount": {
      "description": "The number of missing values in the training data.",
      "type": "integer"
    },
    "missingPercentage": {
      "description": "The percentage of missing values in the training data.",
      "maximum": 1,
      "minimum": 0,
      "type": "number"
    },
    "tasks": {
      "additionalProperties": {
        "properties": {
          "descriptions": {
            "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "name": {
            "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.",
            "type": "string"
          }
        },
        "required": [
          "descriptions",
          "name"
        ],
        "type": "object"
      },
      "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.",
      "type": "object"
    },
    "type": {
      "description": "The type of the feature, e.g., `Categorical` or `Numeric`.",
      "type": "string"
    }
  },
  "required": [
    "feature",
    "missingCount",
    "missingPercentage",
    "tasks",
    "type"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| feature | string | true |  | The name of the feature. |
| missingCount | integer | true |  | The number of missing values in the training data. |
| missingPercentage | number | true | maximum: 1minimum: 0 | The percentage of missing values in the training data. |
| tasks | object | true |  | Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form task described. |
| » additionalProperties | PerFeatureTaskMissingReport | false |  | none |
| type | string | true |  | The type of the feature, e.g., Categorical or Numeric. |

## PerFeatureTaskMissingReport

```
{
  "properties": {
    "descriptions": {
      "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.",
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "name": {
      "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.",
      "type": "string"
    }
  },
  "required": [
    "descriptions",
    "name"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| descriptions | [string] | true |  | Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored. |
| name | string | true |  | Task name, e.g., 'Ordinal encoding of categorical variables'. |

## PlotDataResponse

```
{
  "properties": {
    "actual": {
      "description": "The actual value of the target variable for the specified row.",
      "type": "number"
    },
    "predicted": {
      "description": "The predicted value of the target by the solution for the specified row.",
      "type": "number"
    },
    "row": {
      "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.",
      "type": "integer"
    }
  },
  "required": [
    "actual",
    "predicted",
    "row"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| actual | number | true |  | The actual value of the target variable for the specified row. |
| predicted | number | true |  | The predicted value of the target by the solution for the specified row. |
| row | integer | true |  | The row number from the raw source data. Used as the X axis for the plot when rendered in the web application. |

## PredictionIntervalsCreate

```
{
  "properties": {
    "percentiles": {
      "description": "The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time.",
      "items": {
        "exclusiveMinimum": 0,
        "maximum": 100,
        "type": "integer"
      },
      "maxItems": 1,
      "minItems": 1,
      "type": "array"
    }
  },
  "required": [
    "percentiles"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| percentiles | [integer] | true | maxItems: 1minItems: 1 | The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time. |

## PredictionIntervalsCreateResponse

```
{
  "properties": {
    "message": {
      "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.",
      "type": "string"
    }
  },
  "required": [
    "message"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| message | string | true |  | Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. |

## PredictionIntervalsListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "A descending-ordered array of already-calculated prediction intervals percentiles.",
      "items": {
        "exclusiveMinimum": 0,
        "maximum": 100,
        "type": "integer"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL of the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL of the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "totalCount": {
      "description": "The total number of items across all pages.",
      "type": "integer"
    }
  },
  "required": [
    "data",
    "next",
    "previous",
    "totalCount"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | false |  | The number of items returned on this page. |
| data | [integer] | true |  | A descending-ordered array of already-calculated prediction intervals percentiles. |
| next | string,null(uri) | true |  | The URL of the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL of the previous page (if null, there is no previous page). |
| totalCount | integer | true |  | The total number of items across all pages. |

## PrepareForDeployment

```
{
  "properties": {
    "modelId": {
      "description": "The model to prepare for deployment.",
      "type": "string"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string | true |  | The model to prepare for deployment. |

## PrimeFileCreate

```
{
  "properties": {
    "language": {
      "description": "The desired language of the generated code.",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The Prime model to generate code for.",
      "type": "string"
    }
  },
  "required": [
    "language",
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| language | string | true |  | The desired language of the generated code. |
| modelId | string | true |  | The Prime model to generate code for. |

### Enumerated Values

| Property | Value |
| --- | --- |
| language | [Python, Java] |

## PrimeFileListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.",
      "items": {
        "properties": {
          "id": {
            "description": "The ID of the file.",
            "type": "string"
          },
          "isValid": {
            "description": "Whether the code passed basic validation checks.",
            "type": "boolean"
          },
          "language": {
            "description": "The language the code is written in (e.g., Python).",
            "enum": [
              "Python",
              "Java"
            ],
            "type": "string"
          },
          "modelId": {
            "description": "The ID of the Prime model.",
            "type": "string"
          },
          "parentModelId": {
            "description": "The ID of the model this code approximates.",
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project the file belongs to.",
            "type": "string"
          },
          "rulesetId": {
            "description": "The ID of the ruleset this code uses to approximate the parent model.",
            "type": "integer"
          }
        },
        "required": [
          "id",
          "isValid",
          "language",
          "modelId",
          "parentModelId",
          "projectId",
          "rulesetId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | true |  | The number of items returned on this page. |
| data | [PrimeFileResponse] | true |  | Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/. |
| next | string,null(uri) | true |  | URL pointing to the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | URL pointing to the previous page (if null, there is no previous page). |

## PrimeFileResponse

```
{
  "properties": {
    "id": {
      "description": "The ID of the file.",
      "type": "string"
    },
    "isValid": {
      "description": "Whether the code passed basic validation checks.",
      "type": "boolean"
    },
    "language": {
      "description": "The language the code is written in (e.g., Python).",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the Prime model.",
      "type": "string"
    },
    "parentModelId": {
      "description": "The ID of the model this code approximates.",
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project the file belongs to.",
      "type": "string"
    },
    "rulesetId": {
      "description": "The ID of the ruleset this code uses to approximate the parent model.",
      "type": "integer"
    }
  },
  "required": [
    "id",
    "isValid",
    "language",
    "modelId",
    "parentModelId",
    "projectId",
    "rulesetId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| id | string | true |  | The ID of the file. |
| isValid | boolean | true |  | Whether the code passed basic validation checks. |
| language | string | true |  | The language the code is written in (e.g., Python). |
| modelId | string | true |  | The ID of the Prime model. |
| parentModelId | string | true |  | The ID of the model this code approximates. |
| projectId | string | true |  | The ID of the project the file belongs to. |
| rulesetId | integer | true |  | The ID of the ruleset this code uses to approximate the parent model. |

### Enumerated Values

| Property | Value |
| --- | --- |
| language | [Python, Java] |

## PrimeInfoRetrieveResponse

```
{
  "properties": {
    "canMakePrime": {
      "description": "Indicates whether the requested model is a valid input for creating a Prime model.",
      "type": "boolean"
    },
    "message": {
      "description": "May contain details about why a model is not eligible for DataRobot Prime.",
      "type": "string"
    },
    "messageId": {
      "description": "An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models.",
      "type": "integer"
    }
  },
  "required": [
    "canMakePrime",
    "message",
    "messageId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| canMakePrime | boolean | true |  | Indicates whether the requested model is a valid input for creating a Prime model. |
| message | string | true |  | May contain details about why a model is not eligible for DataRobot Prime. |
| messageId | integer | true |  | An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models. |

## PrimeModelCreatePayload

```
{
  "properties": {
    "parentModelId": {
      "description": "The model being approximated.",
      "type": "string"
    },
    "rulesetId": {
      "description": "The ID of the ruleset to use.",
      "type": "integer"
    }
  },
  "required": [
    "parentModelId",
    "rulesetId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| parentModelId | string | true |  | The model being approximated. |
| rulesetId | integer | true |  | The ID of the ruleset to use. |

## PrimeModelDetailsRetrieveResponse

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "dataSelectionMethod": {
      "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
      "enum": [
        "duration",
        "rowCount",
        "selectedDateRange",
        "useProjectSettings"
      ],
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "hasFinetuners": {
      "description": "Whether a model has fine tuners.",
      "type": "boolean"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isAugmented": {
      "description": "Whether a model was trained using augmentation.",
      "type": "boolean"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isNClustersDynamicallyDetermined": {
      "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "lifecycle": {
      "description": "Object returning model lifecycle.",
      "properties": {
        "reason": {
          "description": "The reason for the lifecycle stage. None if the model is active.",
          "type": [
            "string",
            "null"
          ],
          "x-versionadded": "v2.30"
        },
        "stage": {
          "description": "The model lifecycle stage.",
          "enum": [
            "active",
            "deprecated",
            "disabled"
          ],
          "type": "string",
          "x-versionadded": "v2.30"
        }
      },
      "required": [
        "reason",
        "stage"
      ],
      "type": "object"
    },
    "linkFunction": {
      "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
      "type": "string",
      "x-versionadded": "v2.21"
    },
    "modelFamilyFullName": {
      "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.21"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ]
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "predictionThreshold": {
      "description": "threshold used for binary classification in predictions.",
      "maximum": 1,
      "minimum": 0,
      "type": "number",
      "x-versionadded": "v2.13"
    },
    "predictionThresholdReadOnly": {
      "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
      "type": "boolean",
      "x-versionadded": "v2.13"
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "ruleCount": {
      "description": "The number of rules used to create this model.",
      "type": "integer"
    },
    "rulesetId": {
      "description": "The ID of the ruleset this model uses.",
      "type": "integer"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "samplingMethod": {
      "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "score": {
      "description": "The validation score of the model's ruleset.",
      "type": "number"
    },
    "supportsComposableMl": {
      "description": "indicates whether this model is supported in Composable ML.",
      "type": "boolean",
      "x-versionadded": "2.26"
    },
    "supportsMonotonicConstraints": {
      "description": "whether this model supports enforcing monotonic constraints",
      "type": "boolean",
      "x-versionadded": "v2.21"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingDuration": {
      "description": "the duration spanned by the dates in the partition column for the data used to train the model",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingEndDate": {
      "description": "the end date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "trainingStartDate": {
      "description": "the start date of the dates in the partition column for the data used to train the model",
      "format": "date-time",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "lifecycle",
    "linkFunction",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelFamilyFullName",
    "modelNumber",
    "modelType",
    "monotonicDecreasingFeaturelistId",
    "monotonicIncreasingFeaturelistId",
    "parentModelId",
    "predictionThreshold",
    "predictionThresholdReadOnly",
    "processes",
    "projectId",
    "ruleCount",
    "rulesetId",
    "samplePct",
    "score",
    "supportsComposableMl",
    "supportsMonotonicConstraints",
    "trainingDuration",
    "trainingEndDate",
    "trainingRowCount",
    "trainingStartDate"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| dataSelectionMethod | string | false |  | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| hasFinetuners | boolean | false |  | Whether a model has fine tuners. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isAugmented | boolean | false |  | Whether a model was trained using augmentation. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isNClustersDynamicallyDetermined | boolean | false |  | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| lifecycle | ModelLifecycle | true |  | Object returning model lifecycle. |
| linkFunction | string,null | true |  | The link function the final modeler uses in the blueprint. If no link function exists, returns null. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The family the model belongs to, e.g., SVM, GBM, etc. |
| modelFamilyFullName | string | true |  | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| monotonicDecreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | true |  | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer,null | false |  | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. |
| predictionThresholdReadOnly | boolean | true |  | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| ruleCount | integer | true |  | The number of rules used to create this model. |
| rulesetId | integer | true |  | The ID of the ruleset this model uses. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| samplingMethod | string | false |  | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. |
| score | number | true |  | The validation score of the model's ruleset. |
| supportsComposableMl | boolean | true |  | indicates whether this model is supported in Composable ML. |
| supportsMonotonicConstraints | boolean | true |  | whether this model supports enforcing monotonic constraints |
| timeWindowSamplePct | integer,null | false |  | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. |
| trainingDuration | string,null | true |  | the duration spanned by the dates in the partition column for the data used to train the model |
| trainingEndDate | string,null(date-time) | true |  | the end date of the dates in the partition column for the data used to train the model |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |
| trainingStartDate | string,null(date-time) | true |  | the start date of the dates in the partition column for the data used to train the model |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |
| samplingMethod | [random, latest] |

## PrimeModelListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.",
      "items": {
        "properties": {
          "blenderModels": {
            "description": "Models that are in the blender.",
            "items": {
              "type": "integer"
            },
            "maxItems": 100,
            "type": "array",
            "x-versionadded": "v2.36"
          },
          "blueprintId": {
            "description": "The blueprint used to construct the model.",
            "type": "string"
          },
          "dataSelectionMethod": {
            "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.",
            "enum": [
              "duration",
              "rowCount",
              "selectedDateRange",
              "useProjectSettings"
            ],
            "type": "string"
          },
          "externalPredictionModel": {
            "description": "If the model is an external prediction model.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "featurelistId": {
            "description": "The ID of the feature list used by the model.",
            "type": [
              "string",
              "null"
            ]
          },
          "featurelistName": {
            "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
            "type": [
              "string",
              "null"
            ]
          },
          "frozenPct": {
            "description": "The training percent used to train the frozen model.",
            "type": [
              "number",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "hasCodegen": {
            "description": "If the model has a codegen JAR file.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "hasFinetuners": {
            "description": "Whether a model has fine tuners.",
            "type": "boolean"
          },
          "icons": {
            "description": "The icons associated with the model.",
            "type": [
              "integer",
              "null"
            ],
            "x-versionadded": "v2.36"
          },
          "id": {
            "description": "The ID of the model.",
            "type": "string"
          },
          "isAugmented": {
            "description": "Whether a model was trained using augmentation.",
            "type": "boolean"
          },
          "isBlender": {
            "description": "If the model is a blender.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isCustom": {
            "description": "If the model contains custom tasks.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isFrozen": {
            "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
            "type": "boolean"
          },
          "isNClustersDynamicallyDetermined": {
            "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.",
            "type": "boolean"
          },
          "isStarred": {
            "description": "Indicates whether the model has been starred.",
            "type": "boolean"
          },
          "isTrainedIntoHoldout": {
            "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
            "type": "boolean"
          },
          "isTrainedIntoValidation": {
            "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
            "type": "boolean"
          },
          "isTrainedOnGpu": {
            "description": "Whether the model was trained using GPU workers.",
            "type": "boolean",
            "x-versionadded": "v2.33"
          },
          "isTransparent": {
            "description": "If the model is a transparent model with exposed coefficients.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "isUserModel": {
            "description": "If the model was created with Composable ML.",
            "type": "boolean",
            "x-versionadded": "v2.36"
          },
          "lifecycle": {
            "description": "Object returning model lifecycle.",
            "properties": {
              "reason": {
                "description": "The reason for the lifecycle stage. None if the model is active.",
                "type": [
                  "string",
                  "null"
                ],
                "x-versionadded": "v2.30"
              },
              "stage": {
                "description": "The model lifecycle stage.",
                "enum": [
                  "active",
                  "deprecated",
                  "disabled"
                ],
                "type": "string",
                "x-versionadded": "v2.30"
              }
            },
            "required": [
              "reason",
              "stage"
            ],
            "type": "object"
          },
          "linkFunction": {
            "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "metrics": {
            "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
            "type": "object"
          },
          "modelCategory": {
            "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
            "enum": [
              "model",
              "prime",
              "blend",
              "combined",
              "incrementalLearning"
            ],
            "type": "string"
          },
          "modelFamily": {
            "description": "The family the model belongs to, e.g., SVM, GBM, etc.",
            "type": "string",
            "x-versionadded": "v2.21"
          },
          "modelFamilyFullName": {
            "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.",
            "type": "string",
            "x-versionadded": "v2.31"
          },
          "modelNumber": {
            "description": "The model number from the Leaderboard.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "modelType": {
            "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
            "type": "string"
          },
          "monotonicDecreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "monotonicIncreasingFeaturelistId": {
            "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
            "type": [
              "string",
              "null"
            ],
            "x-versionadded": "v2.21"
          },
          "nClusters": {
            "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
            "type": [
              "integer",
              "null"
            ]
          },
          "parentModelId": {
            "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
            "type": [
              "string",
              "null"
            ]
          },
          "predictionThreshold": {
            "description": "threshold used for binary classification in predictions.",
            "maximum": 1,
            "minimum": 0,
            "type": "number",
            "x-versionadded": "v2.13"
          },
          "predictionThresholdReadOnly": {
            "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.",
            "type": "boolean",
            "x-versionadded": "v2.13"
          },
          "processes": {
            "description": "The list of processes used by the model.",
            "items": {
              "type": "string"
            },
            "maxItems": 100,
            "type": "array"
          },
          "projectId": {
            "description": "The ID of the project to which the model belongs.",
            "type": "string"
          },
          "ruleCount": {
            "description": "The number of rules used to create this model.",
            "type": "integer"
          },
          "rulesetId": {
            "description": "The ID of the ruleset this model uses.",
            "type": "integer"
          },
          "samplePct": {
            "description": "The percentage of the dataset used in training the model.",
            "exclusiveMinimum": 0,
            "type": [
              "number",
              "null"
            ]
          },
          "samplingMethod": {
            "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.",
            "enum": [
              "random",
              "latest"
            ],
            "type": "string"
          },
          "score": {
            "description": "The validation score of the model's ruleset.",
            "type": "number"
          },
          "supportsComposableMl": {
            "description": "indicates whether this model is supported in Composable ML.",
            "type": "boolean",
            "x-versionadded": "2.26"
          },
          "supportsMonotonicConstraints": {
            "description": "whether this model supports enforcing monotonic constraints",
            "type": "boolean",
            "x-versionadded": "v2.21"
          },
          "timeWindowSamplePct": {
            "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.",
            "exclusiveMaximum": 100,
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingDuration": {
            "description": "the duration spanned by the dates in the partition column for the data used to train the model",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingEndDate": {
            "description": "the end date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          },
          "trainingRowCount": {
            "description": "The number of rows used to train the model.",
            "exclusiveMinimum": 0,
            "type": [
              "integer",
              "null"
            ]
          },
          "trainingStartDate": {
            "description": "the start date of the dates in the partition column for the data used to train the model",
            "format": "date-time",
            "type": [
              "string",
              "null"
            ]
          }
        },
        "required": [
          "blenderModels",
          "blueprintId",
          "externalPredictionModel",
          "featurelistId",
          "featurelistName",
          "frozenPct",
          "hasCodegen",
          "icons",
          "id",
          "isBlender",
          "isCustom",
          "isFrozen",
          "isStarred",
          "isTrainedIntoHoldout",
          "isTrainedIntoValidation",
          "isTrainedOnGpu",
          "isTransparent",
          "isUserModel",
          "lifecycle",
          "linkFunction",
          "metrics",
          "modelCategory",
          "modelFamily",
          "modelFamilyFullName",
          "modelNumber",
          "modelType",
          "monotonicDecreasingFeaturelistId",
          "monotonicIncreasingFeaturelistId",
          "parentModelId",
          "predictionThreshold",
          "predictionThresholdReadOnly",
          "processes",
          "projectId",
          "ruleCount",
          "rulesetId",
          "samplePct",
          "score",
          "supportsComposableMl",
          "supportsMonotonicConstraints",
          "trainingDuration",
          "trainingEndDate",
          "trainingRowCount",
          "trainingStartDate"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | true |  | The number of items returned on this page. |
| data | [PrimeModelDetailsRetrieveResponse] | true |  | Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/. |
| next | string,null(uri) | true |  | URL pointing to the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | URL pointing to the previous page (if null, there is no previous page). |

## PrimeRulesetsCreatePayload

```
{
  "type": "object"
}
```

### Properties

None

## PrimeRulesetsListResponse

```
{
  "properties": {
    "modelId": {
      "description": "The ID of the Prime model using this ruleset (if it exists) or null.",
      "type": "string"
    },
    "parentModelId": {
      "description": "The ID of the model this ruleset approximates.",
      "type": "string"
    },
    "projectId": {
      "description": "The project this ruleset belongs to.",
      "type": "string"
    },
    "ruleCount": {
      "description": "The number of rules used by this ruleset.",
      "type": "integer"
    },
    "rulesetId": {
      "description": "The ID of the ruleset.",
      "type": "integer"
    },
    "score": {
      "description": "The validation score of the ruleset.",
      "type": "number"
    }
  },
  "required": [
    "modelId",
    "parentModelId",
    "projectId",
    "ruleCount",
    "rulesetId",
    "score"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string | true |  | The ID of the Prime model using this ruleset (if it exists) or null. |
| parentModelId | string | true |  | The ID of the model this ruleset approximates. |
| projectId | string | true |  | The project this ruleset belongs to. |
| ruleCount | integer | true |  | The number of rules used by this ruleset. |
| rulesetId | integer | true |  | The ID of the ruleset. |
| score | number | true |  | The validation score of the ruleset. |

## Reasons

```
{
  "description": "Information on why the capability is unsupported for the model.",
  "properties": {
    "supportsAccuracyOverTime": {
      "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsAnomalyAssessment": {
      "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsAnomalyOverTime": {
      "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsClusterInsights": {
      "description": "If present, the reason why Cluster Insights cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsConfusionMatrix": {
      "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "supportsDocumentTextExtractionSampleInsight": {
      "description": "If present, the reason document text extraction sample insights are not supported for the model.",
      "type": "string",
      "x-versionadded": "v2.29"
    },
    "supportsForecastAccuracy": {
      "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsForecastVsActual": {
      "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsImageActivationMaps": {
      "description": "If present, the reason image activation maps are not supported for the model.",
      "type": "string"
    },
    "supportsImageEmbedding": {
      "description": "If present, the reason image embeddings are not supported for the model.",
      "type": "string"
    },
    "supportsLiftChart": {
      "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.",
      "type": "string",
      "x-versionadded": "v2.31"
    },
    "supportsPeriodAccuracy": {
      "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionExplanations": {
      "description": "If present, the reason why Prediction Explanations cannot be computed for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsPredictionIntervals": {
      "description": "If present, the reason why Prediction Intervals cannot be computed for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsResiduals": {
      "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.",
      "type": "string",
      "x-versionadded": "v2.30"
    },
    "supportsRocCurve": {
      "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.",
      "type": "string",
      "x-versionadded": "v2.32"
    },
    "supportsSeriesInsights": {
      "description": "If present, the reason why Series Insights cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    },
    "supportsStability": {
      "description": "If present, the reason why Stability plots cannot be generated for the model.",
      "type": "string",
      "x-versionadded": "v2.34"
    }
  },
  "type": "object"
}
```

Information on why the capability is unsupported for the model.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| supportsAccuracyOverTime | string | false |  | If present, the reason why Accuracy Over Time plots cannot be generated for the model. |
| supportsAnomalyAssessment | string | false |  | If present, the reason why the Anomaly Assessment insight cannot be generated for the model. |
| supportsAnomalyOverTime | string | false |  | If present, the reason why Anomaly Over Time plots cannot be generated for the model. |
| supportsClusterInsights | string | false |  | If present, the reason why Cluster Insights cannot be generated for the model. |
| supportsConfusionMatrix | string | false |  | If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions. |
| supportsDocumentTextExtractionSampleInsight | string | false |  | If present, the reason document text extraction sample insights are not supported for the model. |
| supportsForecastAccuracy | string | false |  | If present, the reason why Forecast Accuracy plots cannot be generated for the model. |
| supportsForecastVsActual | string | false |  | If present, the reason why Forecast vs Actual plots cannot be generated for the model. |
| supportsImageActivationMaps | string | false |  | If present, the reason image activation maps are not supported for the model. |
| supportsImageEmbedding | string | false |  | If present, the reason image embeddings are not supported for the model. |
| supportsLiftChart | string | false |  | If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions. |
| supportsPeriodAccuracy | string | false |  | If present, the reason why Period Accuracy insights cannot be generated for the model. |
| supportsPredictionExplanations | string | false |  | If present, the reason why Prediction Explanations cannot be computed for the model. |
| supportsPredictionIntervals | string | false |  | If present, the reason why Prediction Intervals cannot be computed for the model. |
| supportsResiduals | string | false |  | If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions. |
| supportsRocCurve | string | false |  | If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions. |
| supportsSeriesInsights | string | false |  | If present, the reason why Series Insights cannot be generated for the model. |
| supportsStability | string | false |  | If present, the reason why Stability plots cannot be generated for the model. |

## RecommendedModelResponse

```
{
  "properties": {
    "modelId": {
      "description": "The ID of the recommended model.",
      "type": "string"
    },
    "recommendationType": {
      "description": "The type of model recommendation.",
      "enum": [
        "MOSTACCURATE",
        "LIMITEDACCURATE",
        "FASTACCURATE",
        "RECOMMENDEDFORDEPLOYMENT",
        "PREPAREDFORDEPLOYMENT"
      ],
      "type": "string"
    }
  },
  "required": [
    "modelId",
    "recommendationType"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string | true |  | The ID of the recommended model. |
| recommendationType | string | true |  | The type of model recommendation. |

### Enumerated Values

| Property | Value |
| --- | --- |
| recommendationType | [MOSTACCURATE, LIMITEDACCURATE, FASTACCURATE, RECOMMENDEDFORDEPLOYMENT, PREPAREDFORDEPLOYMENT] |

## RetrainDatetimeModel

```
{
  "properties": {
    "featurelistId": {
      "description": "If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model.",
      "type": "string"
    },
    "modelId": {
      "description": "The ID of an existing model to use as the source for the training parameters.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string",
      "x-versionadded": "v2.20"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "duration",
      "type": "string"
    },
    "trainingEndDate": {
      "description": "A datetime string representing the end date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.",
      "format": "date-time",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingStartDate": {
      "description": "A datetime string representing the start date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.",
      "format": "date-time",
      "type": "string"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "type": "boolean",
      "x-versionadded": "v2.24"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| featurelistId | string | false |  | If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model. |
| modelId | string | true |  | The ID of an existing model to use as the source for the training parameters. |
| monotonicDecreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| samplingMethod | string | false |  | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. |
| timeWindowSamplePct | integer | false |  | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. |
| trainingDuration | string(duration) | false |  | A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| trainingEndDate | string(date-time) | false |  | A datetime string representing the end date of the data to use for training this model. Note that only one of trainingDuration or trainingRowCount or trainingStartDate and trainingEndDate should be specified. If trainingStartDate and trainingEndDate are specified, the source model must be frozen. |
| trainingRowCount | integer | false |  | The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| trainingStartDate | string(date-time) | false |  | A datetime string representing the start date of the data to use for training this model. Note that only one of trainingDuration or trainingRowCount or trainingStartDate and trainingEndDate should be specified. If trainingStartDate and trainingEndDate are specified, the source model must be frozen. |
| useProjectSettings | boolean | false |  | If True, the model will be trained using the previously-specified custom backtest training settings. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |

### Enumerated Values

| Property | Value |
| --- | --- |
| samplingMethod | [random, latest] |

## RetrainModel

```
{
  "properties": {
    "featurelistId": {
      "description": "If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before.",
      "type": "string"
    },
    "modelId": {
      "description": "The model to be retrained.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.23"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used.",
      "exclusiveMinimum": 0,
      "maximum": 100,
      "type": "number"
    },
    "scoringType": {
      "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.",
      "enum": [
        "validation",
        "crossValidation"
      ],
      "type": "string",
      "x-versionadded": "v2.23"
    },
    "trainingRowCount": {
      "description": "The number of rows to use to train the model. If not specified, original model training row count will be used.",
      "exclusiveMinimum": 0,
      "type": "integer"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| featurelistId | string | false |  | If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before. |
| modelId | string | true |  | The model to be retrained. |
| monotonicDecreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. |
| monotonicIncreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. |
| nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| samplePct | number | false | maximum: 100 | The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used. |
| scoringType | string | false |  | Validation is available for any partitioning. If the project uses cross validation, crossValidation may be used to indicate that all available training/validation combinations should be used. |
| trainingRowCount | integer | false |  | The number of rows to use to train the model. If not specified, original model training row count will be used. |

### Enumerated Values

| Property | Value |
| --- | --- |
| scoringType | [validation, crossValidation] |

## RuleFitCodeFileCreate

```
{
  "properties": {
    "language": {
      "description": "The desired language of the generated code.",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The RuleFit model to generate code for.",
      "type": "string"
    }
  },
  "required": [
    "language",
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| language | string | true |  | The desired language of the generated code. |
| modelId | string | true |  | The RuleFit model to generate code for. |

### Enumerated Values

| Property | Value |
| --- | --- |
| language | [Python, Java] |

## RuleFitCodeFileListResponse

```
{
  "properties": {
    "count": {
      "description": "The number of items returned on this page.",
      "type": "integer"
    },
    "data": {
      "description": "Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/.",
      "items": {
        "properties": {
          "id": {
            "description": "The ID of the file.",
            "type": "string"
          },
          "isValid": {
            "description": "Whether the code passed basic validation checks.",
            "type": "boolean"
          },
          "language": {
            "description": "The language the code is written in (e.g., Python).",
            "enum": [
              "Python",
              "Java"
            ],
            "type": "string"
          },
          "modelId": {
            "description": "The ID of the RuleFit model.",
            "type": "string"
          },
          "projectId": {
            "description": "The ID of the project the file belongs to.",
            "type": "string"
          }
        },
        "required": [
          "id",
          "isValid",
          "language",
          "modelId",
          "projectId"
        ],
        "type": "object"
      },
      "type": "array"
    },
    "next": {
      "description": "The URL pointing to the next page (if null, there is no next page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    },
    "previous": {
      "description": "The URL pointing to the previous page (if null, there is no previous page).",
      "format": "uri",
      "type": [
        "string",
        "null"
      ]
    }
  },
  "required": [
    "count",
    "data",
    "next",
    "previous"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| count | integer | true |  | The number of items returned on this page. |
| data | [RuleFitCodeFileResponse] | true |  | Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/. |
| next | string,null(uri) | true |  | The URL pointing to the next page (if null, there is no next page). |
| previous | string,null(uri) | true |  | The URL pointing to the previous page (if null, there is no previous page). |

## RuleFitCodeFileResponse

```
{
  "properties": {
    "id": {
      "description": "The ID of the file.",
      "type": "string"
    },
    "isValid": {
      "description": "Whether the code passed basic validation checks.",
      "type": "boolean"
    },
    "language": {
      "description": "The language the code is written in (e.g., Python).",
      "enum": [
        "Python",
        "Java"
      ],
      "type": "string"
    },
    "modelId": {
      "description": "The ID of the RuleFit model.",
      "type": "string"
    },
    "projectId": {
      "description": "The ID of the project the file belongs to.",
      "type": "string"
    }
  },
  "required": [
    "id",
    "isValid",
    "language",
    "modelId",
    "projectId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| id | string | true |  | The ID of the file. |
| isValid | boolean | true |  | Whether the code passed basic validation checks. |
| language | string | true |  | The language the code is written in (e.g., Python). |
| modelId | string | true |  | The ID of the RuleFit model. |
| projectId | string | true |  | The ID of the project the file belongs to. |

### Enumerated Values

| Property | Value |
| --- | --- |
| language | [Python, Java] |

## SegmentChampionModelUpdate

```
{
  "properties": {
    "clone": {
      "default": false,
      "description": "Clone current combined model and assign champion to the new combined model.",
      "type": "boolean",
      "x-versionadded": "v2.29"
    },
    "modelId": {
      "description": "The ID of segment champion model.",
      "type": "string"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| clone | boolean | false |  | Clone current combined model and assign champion to the new combined model. |
| modelId | string | true |  | The ID of segment champion model. |

## SegmentChampionModelUpdateResponse

```
{
  "properties": {
    "combinedModelId": {
      "description": "The ID of the combined model that has been updated.",
      "type": "string"
    }
  },
  "required": [
    "combinedModelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| combinedModelId | string | true |  | The ID of the combined model that has been updated. |

## SegmentProjectModelResponse

```
{
  "properties": {
    "modelId": {
      "description": "The ID of the segment champion model.",
      "type": [
        "string",
        "null"
      ]
    },
    "projectId": {
      "description": "The ID of the project used for this segment.",
      "type": [
        "string",
        "null"
      ]
    },
    "segment": {
      "description": "Segment name.",
      "type": "string"
    }
  },
  "required": [
    "modelId",
    "projectId",
    "segment"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string,null | true |  | The ID of the segment champion model. |
| projectId | string,null | true |  | The ID of the project used for this segment. |
| segment | string | true |  | Segment name. |

## Select

```
{
  "description": "Indicates that the value can be one selected from a list of known values.",
  "properties": {
    "supportsGridSearch": {
      "description": "When True, Grid Search is supported for this parameter.",
      "type": "boolean"
    },
    "values": {
      "description": "List of valid values for this field.",
      "items": {
        "type": "string"
      },
      "type": "array"
    }
  },
  "required": [
    "supportsGridSearch",
    "values"
  ],
  "type": "object"
}
```

Indicates that the value can be one selected from a list of known values.

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| supportsGridSearch | boolean | true |  | When True, Grid Search is supported for this parameter. |
| values | [string] | true |  | List of valid values for this field. |

## SolutionResponse

```
{
  "properties": {
    "bestModel": {
      "description": "True if this solution generates the best model.",
      "type": "boolean"
    },
    "complexity": {
      "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.",
      "type": "integer"
    },
    "error": {
      "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.",
      "type": [
        "number",
        "null"
      ]
    },
    "eureqaSolutionId": {
      "description": "The ID of the solution.",
      "type": "string"
    },
    "expression": {
      "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.",
      "type": "string"
    },
    "expressionAnnotated": {
      "description": "The `expression`, rendered with additional tags to assist in automatic parsing.",
      "type": "string"
    }
  },
  "required": [
    "bestModel",
    "complexity",
    "error",
    "eureqaSolutionId",
    "expression",
    "expressionAnnotated"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| bestModel | boolean | true |  | True if this solution generates the best model. |
| complexity | integer | true |  | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. |
| error | number,null | true |  | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. |
| eureqaSolutionId | string | true |  | The ID of the solution. |
| expression | string | true |  | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. |
| expressionAnnotated | string | true |  | The expression, rendered with additional tags to assist in automatic parsing. |

## StageCoefficients

```
{
  "properties": {
    "coefficient": {
      "description": "The corresponding value of the coefficient for that stage.",
      "type": "number"
    },
    "stage": {
      "description": "The name of the stage.",
      "type": "string"
    }
  },
  "required": [
    "coefficient",
    "stage"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| coefficient | number | true |  | The corresponding value of the coefficient for that stage. |
| stage | string | true |  | The name of the stage. |

## TrainDatetimeFrozenModel

```
{
  "properties": {
    "modelId": {
      "description": "The ID of an existing model to use as the source for the training parameters.",
      "type": "string"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration for the submitted model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "duration",
      "type": "string"
    },
    "trainingEndDate": {
      "description": "A datetime string representing the end date of the data to use for training this model. If specified, `trainingStartDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "date-time",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used when training this model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "trainingStartDate": {
      "description": "A datetime string representing the start date of the data to use for training this model. If specified, `trainingEndDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "format": "date-time",
      "type": "string"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.",
      "type": "boolean",
      "x-versionadded": "v2.24"
    }
  },
  "required": [
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| modelId | string | true |  | The ID of an existing model to use as the source for the training parameters. |
| samplingMethod | string | false |  | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. |
| timeWindowSamplePct | integer | false |  | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. |
| trainingDuration | string(duration) | false |  | A duration string representing the training duration for the submitted model. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| trainingEndDate | string(date-time) | false |  | A datetime string representing the end date of the data to use for training this model. If specified, trainingStartDate must also be specified. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| trainingRowCount | integer | false |  | The number of rows of data that should be used when training this model. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| trainingStartDate | string(date-time) | false |  | A datetime string representing the start date of the data to use for training this model. If specified, trainingEndDate must also be specified. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |
| useProjectSettings | boolean | false |  | If True, the model will be trained using the previously-specified custom backtest training settings. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. |

### Enumerated Values

| Property | Value |
| --- | --- |
| samplingMethod | [random, latest] |

## TrainDatetimeModel

```
{
  "properties": {
    "blueprintId": {
      "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.",
      "type": "string"
    },
    "featurelistId": {
      "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.18"
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.",
      "type": [
        "string",
        "null"
      ],
      "x-versionadded": "v2.18"
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplingMethod": {
      "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.",
      "enum": [
        "random",
        "latest"
      ],
      "type": "string",
      "x-versionadded": "v2.20"
    },
    "sourceProjectId": {
      "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.",
      "type": "string"
    },
    "timeWindowSamplePct": {
      "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.",
      "exclusiveMaximum": 100,
      "exclusiveMinimum": 0,
      "type": "integer",
      "x-versionadded": "v2.7"
    },
    "trainingDuration": {
      "description": "A duration string representing the training duration for the submitted model.",
      "format": "duration",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "The number of rows of data that should be used when training this model.",
      "exclusiveMinimum": 0,
      "type": "integer"
    },
    "useProjectSettings": {
      "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings.",
      "type": "boolean",
      "x-versionadded": "v2.19"
    }
  },
  "required": [
    "blueprintId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blueprintId | string | true |  | The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models. |
| featurelistId | string | false |  | If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used. |
| monotonicDecreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects. |
| monotonicIncreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects. |
| nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| samplingMethod | string | false |  | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. |
| sourceProjectId | string | false |  | The project the blueprint comes from. Required only if the blueprintId comes from a different project. |
| timeWindowSamplePct | integer | false |  | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. |
| trainingDuration | string(duration) | false |  | A duration string representing the training duration for the submitted model. |
| trainingRowCount | integer | false |  | The number of rows of data that should be used when training this model. |
| useProjectSettings | boolean | false |  | If True, the model will be trained using the previously-specified custom backtest training settings. |

### Enumerated Values

| Property | Value |
| --- | --- |
| samplingMethod | [random, latest] |

## TrainIncrementalLearningModel

```
{
  "properties": {
    "dataStageCompression": {
      "description": "The file compression for the data stage. Only supports CSV storage type.",
      "enum": [
        "zip",
        "bz2",
        "gzip"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageDelimiter": {
      "description": "The file delimiter for the data stage specified as a string.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageEncoding": {
      "description": "The file encoding for the data stage.",
      "enum": [
        "UTF-8",
        "ASCII",
        "WINDOWS-1252"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageId": {
      "description": "The data stage ID. The data stage must be finalized and not expired.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "dataStageStorageType": {
      "default": "csv",
      "description": "The file type of the data stage contents.",
      "enum": [
        "csv",
        "parquet"
      ],
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "modelId": {
      "description": "The parent model ID. This model must support incremental learning.",
      "type": "string",
      "x-versionadded": "v2.33"
    },
    "trainingDataName": {
      "description": "String identifier for the current iteration.",
      "maxLength": 500,
      "type": "string",
      "x-versionadded": "v2.33"
    }
  },
  "required": [
    "dataStageId",
    "modelId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| dataStageCompression | string | false |  | The file compression for the data stage. Only supports CSV storage type. |
| dataStageDelimiter | string | false |  | The file delimiter for the data stage specified as a string. |
| dataStageEncoding | string | false |  | The file encoding for the data stage. |
| dataStageId | string | true |  | The data stage ID. The data stage must be finalized and not expired. |
| dataStageStorageType | string | false |  | The file type of the data stage contents. |
| modelId | string | true |  | The parent model ID. This model must support incremental learning. |
| trainingDataName | string | false | maxLength: 500 | String identifier for the current iteration. |

### Enumerated Values

| Property | Value |
| --- | --- |
| dataStageCompression | [zip, bz2, gzip] |
| dataStageEncoding | [UTF-8, ASCII, WINDOWS-1252] |
| dataStageStorageType | [csv, parquet] |

## TrainModel

```
{
  "properties": {
    "blueprintId": {
      "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.",
      "type": "string"
    },
    "featurelistId": {
      "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.",
      "type": "string"
    },
    "monotonicDecreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.",
      "type": [
        "string",
        "null"
      ]
    },
    "monotonicIncreasingFeaturelistId": {
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.",
      "type": [
        "string",
        "null"
      ]
    },
    "nClusters": {
      "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.",
      "maximum": 100,
      "minimum": 2,
      "type": "integer",
      "x-versionadded": "v2.27"
    },
    "samplePct": {
      "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0 and 100.",
      "exclusiveMinimum": 0,
      "maximum": 100,
      "type": "number"
    },
    "scoringType": {
      "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.",
      "enum": [
        "validation",
        "crossValidation"
      ],
      "type": "string"
    },
    "sourceProjectId": {
      "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.",
      "type": "string"
    },
    "trainingRowCount": {
      "description": "An integer representing the number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.",
      "type": "integer"
    }
  },
  "required": [
    "blueprintId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blueprintId | string | true |  | The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models. |
| featurelistId | string | false |  | If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used. |
| monotonicDecreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. |
| monotonicIncreasingFeaturelistId | string,null | false |  | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. |
| nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. |
| samplePct | number | false | maximum: 100 | The percentage of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. The specified percentage should be between 0 and 100. |
| scoringType | string | false |  | Validation is available for any partitioning. If the project uses cross validation, crossValidation may be used to indicate that all available training/validation combinations should be used. |
| sourceProjectId | string | false |  | The project the blueprint comes from. Required only if the blueprintId comes from a different project. |
| trainingRowCount | integer | false |  | An integer representing the number of rows of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. |

### Enumerated Values

| Property | Value |
| --- | --- |
| scoringType | [validation, crossValidation] |

## TrainingInfoResponse

```
{
  "description": "holdout and prediction training data details",
  "properties": {
    "holdoutTrainingDuration": {
      "description": "the duration of the data used to train a model to score the holdout",
      "format": "duration",
      "type": "string"
    },
    "holdoutTrainingEndDate": {
      "description": "the end date of the data used to train a model to score the holdout",
      "format": "date-time",
      "type": "string"
    },
    "holdoutTrainingRowCount": {
      "description": "the number of rows used to train a model to score the holdout",
      "type": "integer"
    },
    "holdoutTrainingStartDate": {
      "description": "the start date of data used to train a model to score the holdout",
      "format": "date-time",
      "type": "string"
    },
    "predictionTrainingDuration": {
      "description": "the duration of the data used to train a model to make predictions",
      "format": "duration",
      "type": "string"
    },
    "predictionTrainingEndDate": {
      "description": "the end date of the data used to train a model to make predictions",
      "format": "date-time",
      "type": "string"
    },
    "predictionTrainingRowCount": {
      "description": "the number of rows used to train a model to make predictions",
      "type": "integer"
    },
    "predictionTrainingStartDate": {
      "description": "the start date of data used to train a model to make predictions",
      "format": "date-time",
      "type": "string"
    }
  },
  "required": [
    "holdoutTrainingDuration",
    "holdoutTrainingEndDate",
    "holdoutTrainingRowCount",
    "holdoutTrainingStartDate",
    "predictionTrainingDuration",
    "predictionTrainingEndDate",
    "predictionTrainingRowCount",
    "predictionTrainingStartDate"
  ],
  "type": "object"
}
```

holdout and prediction training data details

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| holdoutTrainingDuration | string(duration) | true |  | the duration of the data used to train a model to score the holdout |
| holdoutTrainingEndDate | string(date-time) | true |  | the end date of the data used to train a model to score the holdout |
| holdoutTrainingRowCount | integer | true |  | the number of rows used to train a model to score the holdout |
| holdoutTrainingStartDate | string(date-time) | true |  | the start date of data used to train a model to score the holdout |
| predictionTrainingDuration | string(duration) | true |  | the duration of the data used to train a model to make predictions |
| predictionTrainingEndDate | string(date-time) | true |  | the end date of the data used to train a model to make predictions |
| predictionTrainingRowCount | integer | true |  | the number of rows used to train a model to make predictions |
| predictionTrainingStartDate | string(date-time) | true |  | the start date of data used to train a model to make predictions |

## Transformations

```
{
  "properties": {
    "name": {
      "description": "The name of the transformation.",
      "type": "string"
    },
    "value": {
      "description": "The value used in carrying it out.",
      "type": "string"
    }
  },
  "required": [
    "name",
    "value"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| name | string | true |  | The name of the transformation. |
| value | string | true |  | The value used in carrying it out. |

## TuningParameter

```
{
  "properties": {
    "parameterId": {
      "description": "ID of the parameter whose value to set.",
      "type": "string"
    },
    "value": {
      "description": "Value for the specified parameter.",
      "oneOf": [
        {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "integer"
            },
            {
              "type": "boolean"
            },
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ]
        },
        {
          "items": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "integer"
              },
              {
                "type": "boolean"
              },
              {
                "type": "number"
              },
              {
                "type": "null"
              }
            ]
          },
          "type": "array"
        }
      ]
    }
  },
  "required": [
    "parameterId",
    "value"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| parameterId | string | true |  | ID of the parameter whose value to set. |
| value | any | true |  | Value for the specified parameter. |

oneOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | any | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | [anyOf] | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

## TuningParameters

```
{
  "properties": {
    "constraints": {
      "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.",
      "properties": {
        "ascii": {
          "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
          "properties": {
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "supportsGridSearch"
          ],
          "type": "object"
        },
        "float": {
          "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).",
          "properties": {
            "max": {
              "description": "Maximum value for the parameter.",
              "type": "number"
            },
            "min": {
              "description": "Minimum value for the parameter.",
              "type": "number"
            },
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "max",
            "min",
            "supportsGridSearch"
          ],
          "type": "object"
        },
        "floatList": {
          "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).",
          "properties": {
            "maxLength": {
              "description": "Maximum permitted length of the list.",
              "minimum": 0,
              "type": "integer"
            },
            "maxVal": {
              "description": "Maximum permitted value.",
              "type": "number"
            },
            "minLength": {
              "description": "Minimum permitted length of the list.",
              "minimum": 0,
              "type": "integer"
            },
            "minVal": {
              "description": "Minimum permitted value.",
              "type": "number"
            },
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "maxLength",
            "maxVal",
            "minLength",
            "minVal",
            "supportsGridSearch"
          ],
          "type": "object"
        },
        "int": {
          "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.",
          "properties": {
            "max": {
              "description": "Maximum value for the parameter.",
              "type": "integer"
            },
            "min": {
              "description": "Minimum value for the parameter.",
              "type": "integer"
            },
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "max",
            "min",
            "supportsGridSearch"
          ],
          "type": "object"
        },
        "intList": {
          "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.",
          "properties": {
            "maxLength": {
              "description": "Maximum permitted length of the list.",
              "minimum": 0,
              "type": "integer"
            },
            "maxVal": {
              "description": "Maximum permitted value.",
              "type": "integer"
            },
            "minLength": {
              "description": "Minimum permitted length of the list.",
              "minimum": 0,
              "type": "integer"
            },
            "minVal": {
              "description": "Minimum permitted value.",
              "type": "integer"
            },
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "maxLength",
            "maxVal",
            "minLength",
            "minVal",
            "supportsGridSearch"
          ],
          "type": "object"
        },
        "select": {
          "description": "Indicates that the value can be one selected from a list of known values.",
          "properties": {
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            },
            "values": {
              "description": "List of valid values for this field.",
              "items": {
                "type": "string"
              },
              "type": "array"
            }
          },
          "required": [
            "supportsGridSearch",
            "values"
          ],
          "type": "object"
        },
        "selectgrid": {
          "description": "Indicates that the value can be one selected from a list of known values.",
          "properties": {
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            },
            "values": {
              "description": "List of valid values for this field.",
              "items": {
                "type": "string"
              },
              "type": "array"
            }
          },
          "required": [
            "supportsGridSearch",
            "values"
          ],
          "type": "object"
        },
        "unicode": {
          "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.",
          "properties": {
            "supportsGridSearch": {
              "description": "When True, Grid Search is supported for this parameter.",
              "type": "boolean"
            }
          },
          "required": [
            "supportsGridSearch"
          ],
          "type": "object"
        }
      },
      "type": "object"
    },
    "currentValue": {
      "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.",
      "oneOf": [
        {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "integer"
            },
            {
              "type": "boolean"
            },
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ]
        },
        {
          "items": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "integer"
              },
              {
                "type": "boolean"
              },
              {
                "type": "number"
              },
              {
                "type": "null"
              }
            ]
          },
          "type": "array"
        }
      ]
    },
    "defaultValue": {
      "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).",
      "oneOf": [
        {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "integer"
            },
            {
              "type": "boolean"
            },
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ]
        },
        {
          "items": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "integer"
              },
              {
                "type": "boolean"
              },
              {
                "type": "number"
              },
              {
                "type": "null"
              }
            ]
          },
          "type": "array"
        }
      ]
    },
    "parameterId": {
      "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.",
      "type": "string"
    },
    "parameterName": {
      "description": "Name of the parameter.",
      "type": "string"
    },
    "taskName": {
      "description": "Human-readable name of the task that this parameter belongs to.",
      "type": "string"
    },
    "vertexId": {
      "description": "Id of the vertex this parameter belongs to.",
      "type": "string",
      "x-versionadded": "v2.29"
    }
  },
  "required": [
    "constraints",
    "currentValue",
    "defaultValue",
    "parameterId",
    "parameterName",
    "taskName",
    "vertexId"
  ],
  "type": "object"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| constraints | Constraints | true |  | Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format. |
| currentValue | any | true |  | The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. |

oneOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | any | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | [anyOf] | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

continued

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| defaultValue | any | true |  | The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value). |

oneOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | any | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

xor

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| » anonymous | [anyOf] | false |  | none |

anyOf

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | string | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | integer | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | boolean | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | number | false |  | none |

or

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| »» anonymous | null | false |  | none |

continued

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| parameterId | string | true |  | Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request. |
| parameterName | string | true |  | Name of the parameter. |
| taskName | string | true |  | Human-readable name of the task that this parameter belongs to. |
| vertexId | string | true |  | Id of the vertex this parameter belongs to. |

## UnsupervisedClusteringModelRecordResponse

```
{
  "properties": {
    "blenderModels": {
      "description": "Models that are in the blender.",
      "items": {
        "type": "integer"
      },
      "maxItems": 100,
      "type": "array",
      "x-versionadded": "v2.36"
    },
    "blueprintId": {
      "description": "The blueprint used to construct the model.",
      "type": "string"
    },
    "externalPredictionModel": {
      "description": "If the model is an external prediction model.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "featurelistId": {
      "description": "The ID of the feature list used by the model.",
      "type": [
        "string",
        "null"
      ]
    },
    "featurelistName": {
      "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.",
      "type": [
        "string",
        "null"
      ]
    },
    "frozenPct": {
      "description": "The training percent used to train the frozen model.",
      "type": [
        "number",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "hasCodegen": {
      "description": "If the model has a codegen JAR file.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "icons": {
      "description": "The icons associated with the model.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.36"
    },
    "id": {
      "description": "The ID of the model.",
      "type": "string"
    },
    "isBlender": {
      "description": "If the model is a blender.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isCustom": {
      "description": "If the model contains custom tasks.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isFrozen": {
      "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.",
      "type": "boolean"
    },
    "isStarred": {
      "description": "Indicates whether the model has been starred.",
      "type": "boolean"
    },
    "isTrainedIntoHoldout": {
      "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.",
      "type": "boolean"
    },
    "isTrainedIntoValidation": {
      "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.",
      "type": "boolean"
    },
    "isTrainedOnGpu": {
      "description": "Whether the model was trained using GPU workers.",
      "type": "boolean",
      "x-versionadded": "v2.33"
    },
    "isTransparent": {
      "description": "If the model is a transparent model with exposed coefficients.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "isUserModel": {
      "description": "If the model was created with Composable ML.",
      "type": "boolean",
      "x-versionadded": "v2.36"
    },
    "metrics": {
      "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.",
      "type": "object"
    },
    "modelCategory": {
      "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.",
      "enum": [
        "model",
        "prime",
        "blend",
        "combined",
        "incrementalLearning"
      ],
      "type": "string"
    },
    "modelFamily": {
      "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).",
      "type": "string"
    },
    "modelNumber": {
      "description": "The model number from the Leaderboard.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    },
    "modelType": {
      "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).",
      "type": "string"
    },
    "numberOfClusters": {
      "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.",
      "type": [
        "integer",
        "null"
      ],
      "x-versionadded": "v2.34"
    },
    "parentModelId": {
      "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.",
      "type": [
        "string",
        "null"
      ]
    },
    "processes": {
      "description": "The list of processes used by the model.",
      "items": {
        "type": "string"
      },
      "maxItems": 100,
      "type": "array"
    },
    "projectId": {
      "description": "The ID of the project to which the model belongs.",
      "type": "string"
    },
    "samplePct": {
      "description": "The percentage of the dataset used in training the model.",
      "exclusiveMinimum": 0,
      "type": [
        "number",
        "null"
      ]
    },
    "trainingRowCount": {
      "description": "The number of rows used to train the model.",
      "exclusiveMinimum": 0,
      "type": [
        "integer",
        "null"
      ]
    }
  },
  "required": [
    "blenderModels",
    "blueprintId",
    "externalPredictionModel",
    "featurelistId",
    "featurelistName",
    "frozenPct",
    "hasCodegen",
    "icons",
    "id",
    "isBlender",
    "isCustom",
    "isFrozen",
    "isStarred",
    "isTrainedIntoHoldout",
    "isTrainedIntoValidation",
    "isTrainedOnGpu",
    "isTransparent",
    "isUserModel",
    "metrics",
    "modelCategory",
    "modelFamily",
    "modelNumber",
    "modelType",
    "numberOfClusters",
    "parentModelId",
    "processes",
    "projectId",
    "samplePct",
    "trainingRowCount"
  ],
  "type": "object",
  "x-versionadded": "v2.34"
}
```

### Properties

| Name | Type | Required | Restrictions | Description |
| --- | --- | --- | --- | --- |
| blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. |
| blueprintId | string | true |  | The blueprint used to construct the model. |
| externalPredictionModel | boolean | true |  | If the model is an external prediction model. |
| featurelistId | string,null | true |  | The ID of the feature list used by the model. |
| featurelistName | string,null | true |  | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. |
| frozenPct | number,null | true |  | The training percent used to train the frozen model. |
| hasCodegen | boolean | true |  | If the model has a codegen JAR file. |
| icons | integer,null | true |  | The icons associated with the model. |
| id | string | true |  | The ID of the model. |
| isBlender | boolean | true |  | If the model is a blender. |
| isCustom | boolean | true |  | If the model contains custom tasks. |
| isFrozen | boolean | true |  | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. |
| isStarred | boolean | true |  | Indicates whether the model has been starred. |
| isTrainedIntoHoldout | boolean | true |  | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. |
| isTrainedIntoValidation | boolean | true |  | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. |
| isTrainedOnGpu | boolean | true |  | Whether the model was trained using GPU workers. |
| isTransparent | boolean | true |  | If the model is a transparent model with exposed coefficients. |
| isUserModel | boolean | true |  | If the model was created with Composable ML. |
| metrics | object | true |  | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. |
| modelCategory | string | true |  | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. |
| modelFamily | string | true |  | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). |
| modelNumber | integer,null | true |  | The model number from the Leaderboard. |
| modelType | string | true |  | Identifies the model (e.g., Nystroem Kernel SVM Regressor). |
| numberOfClusters | integer,null | true |  | The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects. |
| parentModelId | string,null | true |  | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. |
| processes | [string] | true | maxItems: 100 | The list of processes used by the model. |
| projectId | string | true |  | The ID of the project to which the model belongs. |
| samplePct | number,null | true |  | The percentage of the dataset used in training the model. |
| trainingRowCount | integer,null | true |  | The number of rows used to train the model. |

### Enumerated Values

| Property | Value |
| --- | --- |
| modelCategory | [model, prime, blend, combined, incrementalLearning] |

---
