In order to create PredictionExplanations for a particular model and dataset, you must first:
Compute feature impact for the model via
Compute a PredictionExplanationsInitialization for the model via
Compute predictions for the model and dataset via'
After prediction explanations are requested information about them can be accessed using
GetPredictionExplanationsMetadata. Prediction explanations themselves can be accessed
using the functions
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An S3 object of class dataRobotModel like that returned by the function GetModel, or each element of the list returned by the function ListModels.
character. ID of the prediction dataset for which prediction explanations are requested.
integer. Optional. The maximum number of prediction explanations to supply per row of the dataset, default: 3.
numeric. Optional. The lower threshold, below which a prediction must
score in order for prediction explanations to be computed for a row in the dataset. If
numeric. Optional. The high threshold, above which a prediction must score
in order for prediction explanations to be computed. If neither
thresholdLow are optional filters applied to speed up
computation. When at least one is specified, only the selected outlier rows will have
prediction explanations computed. Rows are considered to be outliers if their predicted
value (in case of regression projects) or probability of being the positive
class (in case of classification projects) is less than
threshold_low or greater than
thresholdHigh. If neither is specified, prediction explanations will be computed for
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