Trustworthy Feature Importance Avoids Unrestricted Permutations
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913025896742912 |
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| author | Borgonovo, Emanuele Cappelli, Francesco Lu, Xuefei Plischke, Elmar Rudin, Cynthia |
| author_facet | Borgonovo, Emanuele Cappelli, Francesco Lu, Xuefei Plischke, Elmar Rudin, Cynthia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trustworthy Feature Importance Avoids Unrestricted Permutations Borgonovo, Emanuele Cappelli, Francesco Lu, Xuefei Plischke, Elmar Rudin, Cynthia Machine Learning 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. |
| title | Trustworthy Feature Importance Avoids Unrestricted Permutations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.11253 |