AI RESEARCH
Disentangled Feature Importance
arXiv CS.LG
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ArXi:2507.00260v2 Announce Type: replace-cross Feature importance (FI) measures are widely used to assess the contributions of predictors to an outcome, but they may target different notions of relevance. When predictors are correlated, traditional statistical FI methods are often tailored for feature selection and correlation can therefore be treated as conditional redundancy. By contrast, for model interpretation, FI is naturally defined through marginal predictive relevance.