SHAP Loop End

Calculates the SHAP values by evaluating the predictions your model made in the loop body. For each explained row of interest (rows in the first input table of the SHAP Loop Start node, we will refer to these as ROI), the output table of this node contains d rows where d is the number of predictions your model produces (e.g. one for each class probability in a classification task). The rows consist of four special columns followed by a column for each of your features that hold the SHAP value of that feature for the current prediction column. The special columns are:

• RowId: Holds the RowId of the explained ROI.
• Target: The name of the prediction column that is explained.
• Actual Prediction: The actual prediction for the unaltered ROI.
• Deviation from mean prediction: How much the prediction for this ROI differs from the mean prediction on the sampling table (second input table of the SHAP Loop Start).

The SHAP value for a single feature indicates how much this feature contributed to the deviation from the mean prediction. Provided the explanation set size is large enough, the values for all features should add up to the deviation from the mean prediction.

Input ports

1. Type: Table
   Table containing the rows to explain.
   Table containing predictions for the perturbed rows produced by the Shapley Values Loop Start node

Output ports

1. Type: Table
   Table containing the Shapley Values.
   Table containing the Shapley Values for each feature-prediction combination