  Global feature effect methods, such as partial dependence plots, provide an
intelligible visualization of the expected marginal feature effect. However,
such global feature effect methods can be misleading, as they do not represent
local feature effects of single observations well when feature interactions are
present. We formally introduce generalized additive decomposition of global
effects (GADGET), which is a new framework based on recursive partitioning to
find interpretable regions in the feature space such that the
interaction-related heterogeneity of local feature effects is minimized. We
provide a mathematical foundation of the framework and show that it is
applicable to the most popular methods to visualize marginal feature effects,
namely partial dependence, accumulated local effects, and Shapley additive
explanations (SHAP) dependence. Furthermore, we introduce and validate a new
permutation-based interaction test to detect significant feature interactions
that is applicable to any feature effect method that fits into our proposed
framework. We empirically evaluate the theoretical characteristics of the
proposed methods based on various feature effect methods in different
experimental settings. Moreover, we apply our introduced methodology to three
real-world examples to showcase their usefulness.
