基于特征交互的全局特征效应分解
Decomposing Global Feature Effects Based on Feature Interactions
AI总结：
本文提出GADGET框架，通过递归划分最小化局部特征效应异质性，分解全局特征效应，并引入排列交互检测程序，适用于多种特征效应方法。
摘要：
全局特征效应方法，如部分依赖图，提供了预期边际特征效应的可理解可视化。然而，当存在特征交互时，此类全局特征效应方法可能具有误导性，因为它们不能很好地表示单个观测的局部特征效应。我们正式引入了全局效应的广义加性分解（GADGET），这是一个基于递归划分的新框架，用于在特征空间中找到可解释的区域，使得局部特征效应中与交互相关的异质性最小化。我们为该框架提供了数学基础，并表明它适用于最流行的边际特征效应可视化方法，即部分依赖、累积局部效应和Shapley加性解释（SHAP）依赖。此外，我们引入并验证了一种新的基于排列的交互检测程序，该程序适用于任何符合我们提出框架的特征效应方法。我们基于不同实验设置中的各种特征效应方法，对所提出方法的理论特性进行了实证评估。此外，我们将所引入的方法应用于三个真实世界的示例，以展示其实用性。
英文摘要：
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.
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