Creates table summarizing the top_k positive and top_k negative contributing features to the prediction of a single datapoint.
XGBoost models and CatBoost multiclass classifiers are not currently supported.
pipeline (PipelineBase) – Fitted pipeline whose predictions we want to explain with SHAP.
input_features (ww.DataTable, pd.DataFrame) – Dataframe of features - needs to correspond to data the pipeline was fit on.
top_k (int) – How many of the highest/lowest features to include in the table.
training_data (pd.DataFrame) – Training data the pipeline was fit on.
This is required for non-tree estimators because we need a sample of training data for the KernelSHAP algorithm.
include_shap_values (bool) – Whether the SHAP values should be included in an extra column in the output.
Default is False.
output_format (str) – Either “text” or “dict”. Default is “text”.
str or dict - A report explaining the most positive/negative contributing features to the predictions.