API documentation home > API reference documentation > Batch Prediction API > Output formats

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.

Job parameter	Description	Example value
Output Column Remapping Parameters
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}