Understanding the inner workings of complex machine learning models is a long-standing problem, with recent research focusing primarily on local interpretability. To assess the role of individual input features in a global sense, we propose a new feature importance method, Shapley Additive Global importancE (SAGE), a model-agnostic measure of feature importance based on the predictive power associated with each feature. SAGE relates to prior work through the novel framework of additive importance measures, a perspective that unifies numerous other feature importance methods and shows that only SAGE properly accounts for complex feature interactions. We define SAGE using the Shapley value from cooperative game theory, which leads to numerous intuitive and desirable properties. Our experiments apply SAGE to eight datasets, including MNIST and breast cancer subtype classification, and demonstrate its advantages through quantitative and qualitative evaluations.

本研究探索了通过特征的预测能力定义特征重要性的视角，提出了两种预测能力的概念（模型基础和通用），并通过添加重要性度量的框架来规范化该方法。我们提出了一个模型不可知的方法SAGE，量化预测能力同时考虑特征交互，并发现它比其他方法分配更准确的重要性值。

使用加性重要性度量了解全局特征贡献