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stat.ML2026
Trustworthy Feature Importance Avoids Unrestricted Permutations
Emanuele Borgonovo, Francesco Cappelli, Xuefei Lu +2
Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose…
stat.ML2026
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
Simon D. Nguyen, Hayden McTavish, Kentaro Hoffman +2
Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based d…