  Many important questions about a model cannot be answered just by explaining
how much each feature contributes to its output. To answer a broader set of
questions, we generalize a popular, mathematically well-grounded explanation
technique, Shapley Additive Explanations (SHAP). Our new method - Generalized
Shapley Additive Explanations (G-SHAP) - produces many additional types of
explanations, including: 1) General classification explanations; Why is this
sample more likely to belong to one class rather than another? 2) Intergroup
differences; Why do our model's predictions differ between groups of
observations? 3) Model failure; Why does our model perform poorly on a given
sample? We formally define these types of explanations and illustrate their
practical use on real data.
