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 detection procedure 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.

翻译：全局特征效应方法（如部分依赖图）能够提供预期边际特征效应的可理解可视化。然而，当存在特征交互作用时，此类全局特征效应方法可能产生误导，因为它们无法准确表征单个观测值的局部特征效应。本文正式提出全局效应的广义可加分解（GADGET）框架，该框架基于递归划分在特征空间中寻找可解释区域，以最小化局部特征效应中与交互作用相关的异质性。我们为该框架建立了数学基础，并证明其适用于最主流的边际特征效应可视化方法，包括部分依赖、累积局部效应以及沙普利加性解释（SHAP）依赖图。此外，我们提出并验证了一种新的基于置换的交互作用检测流程，该流程适用于任何符合本框架的特征效应方法。我们通过不同实验场景下的多种特征效应方法，对所提方法的理论特性进行了实证评估。最后，我们将提出的方法论应用于三个实际案例，以展示其应用价值。