提出TRIP测试，检测特征重要性得分是否因特征相关而不可靠。
TRIP: A Nonparametric Test to Diagnose Biased Feature Importance Scores
- 基于置换方法设计无参数检验，识别依赖特征导致的误判
- 在模拟和真实数据中验证可有效发现不可靠的重要度评分
- 适用于高维场景，帮助研究人员判断解释结果可信度
除了准确预测外，理解每个特征对预测结果的贡献——即特征重要性——是机器学习模型的一个理想且必要组成部分。对于随机森林等复杂模型，特征重要性并非内生，需通过外部方法计算。其中，置换特征重要性因高效、模型无关和直观而广受欢迎。然而，当特征存在依赖关系时，置换会生成不合理的样本，导致重要性评估失真。本文提出TRIP（通过置换实现可靠解释的检验），一种假设极少的无参数测试，可检测由模型外推引起的不可靠置换重要性评分。进一步展示了该测试在高维场景下的扩展应用。在模拟数据与实际应用中的实验表明，该测试能可靠识别置换重要性评分失效的情况。
原文摘要 · Abstract (English)
Along with accurate prediction, understanding the contribution of each feature to the making of the prediction, i.e., the importance of the feature, is a desirable and arguably necessary component of a machine learning model. For a complex model such as a random forest, such importances are not innate -- as they are, e.g., with linear regression. Efficient methods have been created to provide such capabilities, with one of the most popular among them being permutation feature importance due to its efficiency, model-agnostic nature, and perceived intuitiveness. However, permutation feature importance has been shown to be misleading in the presence of dependent features as a result of the creation of unrealistic observations when permuting the dependent features. In this work, we develop TRIP (Test for Reliable Interpretation via Permutation), a test requiring minimal assumptions that is able to detect unreliable permutation feature importance scores that are the result of model extrapolation. To build on this, we demonstrate how the test can be complemented in order to allow its use in high dimensional settings. Through testing on simulated data and applications, our results show that the test can be used to reliably detect when permutation feature importance scores are unreliable.
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