Ensembles of Random SHAPs

The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a large number of features. The main idea behind the proposed modifications is to approximate the SHAP by an ensemble of SHAPs with a smaller number of features. According to the first modification, called the ER-SHAP, several features are randomly selected many times from the feature set, and the Shapley values for the features are computed by means of small SHAPs. The explanation results are averaged to obtain the final Shapley values. According to the second modification, called the ERW-SHAP, several points are generated around the explained instance for diversity purposes, and the results of their explanation are combined with weights depending on the distances between the points and the explained instance. The third modification, called the ER-SHAP-RF, uses the random forest for a preliminary explanation of the instances and determines a feature probability distribution which is applied to the selection of the features in the ensemble-based procedure of the ER-SHAP. Many numerical experiments illustrating the proposed modifications demonstrate their efficiency and properties for a local explanation.

Automated summary

This paper proposes ensemble-based modifications to simplify the SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model. The modifications approximate the SHAP by ensembles with a smaller number of features. Three modifications are proposed, namely ER-SHAP, ERW-SHAP, and ER-SHAP-RF. Numerical experiments demonstrate the effectiveness and local interpretability of these modifications.