FEDS Paper: Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Thomas R. Cook, Zach D. Modig, Nathan M. PalmerMachine learning and artificial intelligence are often described as "black boxes." Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME).