论文标题
相对特征的重要性
Relative Feature Importance
论文作者
论文摘要
可解释的机器学习（IML）方法用于洞悉感兴趣的功能与模型性能的相关性。常用的IML方法在考虑隔离中感兴趣的特征，例如置换特征重要性（PFI）还是与所有其余特征变量有关，例如条件特征重要性（CFI）。因此，PFI和CFI固有的扰动机制代表极端参考点。我们介绍了相对特征重要性（RFI），这是PFI和CFI的概括，它允许在PFI与CFI二分法之外更加细微的特征重要性计算。借助RFI，可以评估功能相对于任何其他特征子集的重要性，包括在训练时间无法使用的变量。我们基于对相对特征相关性的含义的详细理论分析得出RFI的一般解释规则，并证明该方法对模拟示例的有用性。
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.
