Creates a report summarizing the top contributing features for each data point in the input features.
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 input data to evaluate the pipeline on.
training_data (ww.DataTable, pd.DataFrame) – Dataframe of data the pipeline was fit on. This can be omitted for pipelines
with tree-based estimators.
top_k_features (int) – How many of the highest/lowest contributing feature to include in the table for each
include_shap_values (bool) – Whether SHAP values should be included in the table. Default is False.
output_format (str) – Either “text” or “dict”. Default is “text”.
The report will include the feature names, prediction contribution, and SHAP Value (optional).