Trustworthy Feature Importance Avoids Unrestricted Permutations
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 13 Apr 2026.
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References · 8
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
- 2019The many Shapley values for model explanationcited by 341
- 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 212
- 2016Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selectioncited by 120
- 2014Controlling the false discovery rate via knockoffscited by 97
- 2019Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importancecited by 85
- 2024Factor Importance Ranking and Selection using Total Indicescited by 1
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