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 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 two real-world examples to showcase their usefulness.

翻译：全局特征效应方法（如部分依赖图）能直观展示期望边际特征效应。然而，当存在特征交互时，此类全局特征效应方法可能产生误导，因其无法准确表征单个观测的局部特征效应。我们正式提出全局效应的广义可加分解方法（GADGET），这是一种基于递归划分的新框架，旨在寻找特征空间中使局部特征效应的交互相关异质性最小化的可解释区域。我们为该框架提供了数学理论基础，并证明其可应用于最流行的边际特征效应可视化方法，即部分依赖图、累积局部效应图及沙普利可加解释（SHAP）依赖图。此外，我们提出一种新的基于置换的交互显著性检验方法，该方法适用于任何符合本框架的特征效应方法。通过在不同实验环境下基于多种特征效应方法，我们实证评估了所提方法的理论特性。同时，我们将该方法应用于两个真实世界案例，以展示其实用价值。