Just add noise: Debiasing tree-based variable importance in mixed data

Jiahe Li; Omar Melikechi

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arXiv · 2609.14083

Just add noise: Debiasing tree-based variable importance in mixed data

Jiahe Li·Omar Melikechi

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Abstract

Variable importance scores from tree-based methods such as random forests favor continuous predictors over categorical ones. We present a theoretical analysis of this bias and propose a simple remedy: add a small amount of noise to each categorical predictor. The correction is demonstrated on a variety of simulated and real-world datasets and combined with integrated path stability selection to perform variable selection with false discovery control for mixed data.

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Jiahe Li, Omar Melikechi. 2026-09-12. Just add noise: Debiasing tree-based variable importance in mixed data. https://arxiv.org/abs/2609.14083

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