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Output format

DataRobot returns predictions in a columnar table format. The columns returned are determined by model type, as described below.

Regression models

Prediction label
Column name <target_name>_PREDICTION
Example name revenue_PREDICTION
Example value 493822.12
Description The predicted value

Binary classification models

Positive label
Column name <target_name>_<positive_label>_PREDICTION
Example name isbadbuy_1_PREDICTION
Example value 0.28
Description The float probability of the positive label
Negative label
Column name <target_name>_<negative_label>_PREDICTION
Example name isbadbuy_0_PREDICTION
Example value 0.72
Description The float probability of the negative label
Prediction label
Column name <target_name>_PREDICTION
Example name isbadbuy_PREDICTION
Example value 0
Description The predicted label of the classification
Threshold label
Column name THRESHOLD
Example name THRESHOLD
Example value 0.5
Description The float prediction threshold used for determining the label
Positive class label
Column name POSITIVE_CLASS
Example name POSITIVE_CLASS
Example value 1
Description The label configured as the positive class

Multiclass classification models

Prediction label
Column name <target_name>_PREDICTION
Example name species_PREDICTION
Example value lion
Description The predicted label of the classification
Prediction class label (for each class)
Column name <target_name>_<class_label>_PREDICTION
Description The float probability for each class
Example classifications
Example name Example value
species_cat_PREDICTION 0.28
species_lion_PREDICTION 0.24
species_lynx_PREDICTION 0.48

Prediction status

Prediction status label
Column name prediction_status
Description A row-by-row status containing either OK or a string error message describing why the prediction did not succeed
Example value Could not convert date field to date format YYYY-MM-DD
Example value OK

Prediction warnings

If prediction warnings are enabled for your job, DataRobot returns an additional column.

Column name IS_OUTLIER_PREDICTION
Description Whether the prediction is outside the calculated prediction boundaries
Example values
Column Example value
IS_OUTLIER_PREDICTION True
IS_OUTLIER_PREDICTION False

Deployment approval status

If the approval workflow is enabled for your deployment, the output schema will contain an extra column showing the deployment approval status.

Deployment status label
Column name DEPLOYMENT_APPROVAL_STATUS
Description Whether the deployment was approved
Example value PENDING

Prediction Explanations

You can request Prediction Explanations be returned with your predictions by setting the maxExplanations job parameter to a non-zero value. You can also set thresholds for computing explanations. If you do not configure a threshold, DataRobot computes explanations for every row.

Prediction Explanation Parameters
Job parameter Description Example value
maxExplanations Optional. Compute up to this number of explanations. 10
thresholdHigh Optional. Limit explanations to predictions above this threshold. 0.5
thresholdLow Optional. Limit explanations to predictions below this threshold. 0.15

If Prediction Explanations are requested, DataRobot returns four extra columns for each explanation, in the format EXPLANATION_<n>_IDENTIFIER (where n is the feature explanation index, from 1 to the maximum number of explanations requested). The returned columns are:

  • EXPLANATION_<n>_FEATURE_NAME: The feature name this explanation covers.
  • EXPLANATION_<n>_STRENGTH: The feature strength as a float.
  • EXPLANATION_<n>_QUALITATIVE_STRENGTH: The feature strength as a string, a plus or minus indicator from +++ to ---.
  • EXPLANATION_<n>_ACTUAL_VALUE: The feature associated with this explanation.

Examples

Name Value
EXPLANATION_1_FEATURE_NAME loan_status
EXPLANATION_1_ACTUAL_VALUE Charged Off
EXPLANATION_1_STRENGTH 1.380291221709652
EXPLANATION_1_QUALITATIVE_STRENGTH +++
Name Value
EXPLANATION_1_FEATURE_NAME loan_status
EXPLANATION_1_ACTUAL_VALUE Fully Paid
EXPLANATION_1_STRENGTH -1.2145340858375335
EXPLANATION_1_QUALITATIVE_STRENGTH ---
Output Filter Parameters
Job parameter Description Example value
includeProbabilities Optional. Include probabilities for all classes; defaults to true. true
includeProbabilitiesClasses Optional. Include only probabilities for classes listed in the given array; defaults to an empty array []. ['setosa', 'versicolor']
includePredictionStatus Optional. Include the prediction_status column in the output; defaults to false. true

Passthrough columns

Passthrough columns you request are passed verbatim. If they conflict with any of the above names, the job is rejected.

Association ID

If your deployment was configured with an Association ID for accuracy, all result sets will have that column passed through from the source data automatically.

Output filters

Use the following job configuration properties to control whether to display only specific class probabilities or none at all.

Output Filter Parameters
Job parameter Description Example value
includeProbabilities Optional. Include probabilities for all classes; defaults to true. true
includeProbabilitiesClasses Optional. Include only probabilities for classes listed in the given array; defaults to an empty array []. ['setosa', 'versicolor']
includePredictionStatus Optional. Include the prediction_status column in the output; defaults to false. true

Note

For binary classification, includeProbabilities also controls the THRESHOLD and >POSITIVE_CLASS columns.

Column name remapping

If your use case has a strict output schema that does not match the DataRobot output, you can rename and remove any columns from the output using the columnNamesRemapping job configuration property.

Output Column Remapping Parameters
Job parameter Description Example value
columnNamesRemapping Optional. An object/dictionary containing a mapping of columns to rename from/to. Set a target column to null or false to ignore it; defaults to an empty set {}. {'isbadbuy_1_PREDICTION': 'prediction', 'isbadbuy_0_PREDICTION': null}

Updated November 16, 2021
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