Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose three new approaches: conditional model reliance and Knockoffs with Gaussian transformation, and restricted ALE plot designs. Theoretical and numerical results show our strategies reduce/eliminate extrapolation.

翻译：基于无限制置换的特征重要性方法因外推误差而存在缺陷；此类误差普遍存在于所有非平凡变量重要性方法中。我们提出三种新方法：基于高斯变换的条件模型依赖与Knockoffs方法，以及受限ALE图设计。理论与数值结果均表明，我们的策略能够减少或消除外推误差。