DataRobot returns predictions in a columnar table format. Each example value is followed by the data type it belongs to. The columns returned are determined by model type, as described below.
DataRobot allows prediction output to many different databases that all have unique versions of a string (e.g., some may call it TEXT while others may call it VARCHAR).
As a result, Datarobot cannot provide implementation-specific data types.
These output columns are available for time series regression, classification, and anomaly detection models.
Time series model columns
Contains the series ID the row belongs to.
Functions as a passthrough column and returns the unaltered column name and values provided in the scoring data.
Contains the forecast point timestamp.
Unless you request historical time series predictions, the output value is the same for all rows with the same forecast point (but different for each unique forecast distance).
Contains the time series timestamp.
Functions as a passthrough column and returns the unaltered column name and values provided in the scoring data. (This returns the same value as the originalFormatTimestamp field returned by time series models.)
Contains the numeric forecast distance returned by time series models.
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
(Optional) Compute up to this number of explanations.
(Optional) Limit explanations to predictions above this threshold.
(Optional) Limit explanations to predictions below this threshold.
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:
Prediction Explanation columns
The feature name this explanation covers.
The feature strength as a float.
The feature strength as a string, a plus or minus indicator from +++ to ---.