Michael Mayer answers a pair of related questions:
Within only a few years, SHAP (Shapley additive explanations) has emerged as the number 1 way to investigate black-box models. The basic idea is to decompose model predictions into additive contributions of the features in a fair way. Studying decompositions of many predictions allows to derive global properties of the model.
What happens if we apply SHAP algorithms to additive models? Why would this ever make sense?
Read on for the answers to these two questions.