不受限制的置换强制外推：变量重要性至少需要一个模型，或者没有自由变量重要性
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
摘要：
本文回顾并反对使用置换和预测（PaP）方法来解释黑盒函数。诸如为随机森林提出的变量重要性度量、部分依赖图和个体条件期望图等方法仍然很受欢迎，因为它们都是模型不可知的，并且只依赖于预先训练的模型输出，这使得它们在计算上很高效，并且在软件中广泛使用。然而，许多研究发现，这些工具可以产生高度误导的诊断，特别是当特征之间存在强烈依赖性时。我们在这里的工作的目的是（i）回顾这一不断增长的文献，（ii）提供这些缺点的进一步证明，以及它们发生的原因的详细解释，以及（iii）倡导涉及额外建模的替代措施。特别是，我们描述了如何打破保持数据中的特征之间的依赖关系，通过迫使原始模型外推到几乎没有数据的区域来过度强调特征空间的稀疏区域。我们探索这些影响在不同的模型设置，并找到支持以前的索赔文献中，PaP指标可以大大过分强调变量的重要性措施和部分依赖图的相关功能。作为一种替代方案，我们讨论并推荐更直接的方法，涉及测量模型性能的变化后，静音的功能正在调查的影响。
英文摘要：
This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures proposed for random forests, partial dependence plots, and individual conditional expectation plots remain popular because they are both model-agnostic and depend only on the pre-trained model output, making them computationally efficient and widely available in software. However, numerous studies have found that these tools can produce diagnostics that are highly misleading, particularly when there is strong dependence among features. The purpose of our work here is to (i) review this growing body of literature, (ii) provide further demonstrations of these drawbacks along with a detailed explanation as to why they occur, and (iii) advocate for alternative measures that involve additional modeling. In particular, we describe how breaking dependencies between features in hold-out data places undue emphasis on sparse regions of the feature space by forcing the original model to extrapolate to regions where there is little to no data. We explore these effects across various model setups and find support for previous claims in the literature that PaP metrics can vastly over-emphasize correlated features in both variable importance measures and partial dependence plots. As an alternative, we discuss and recommend more direct approaches that involve measuring the change in model performance after muting the effects of the features under investigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
