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

The paper investigates the bias in variable importance scores produced by tree-based methods, noting that continuous predictors are favored over categorical ones. It offers a theoretical explanation for this bias and proposes a straightforward fix: adding a small amount of noise to each categorical predictor. The authors validate the correction on both simulated and real-world datasets and integrate it with integrated path stability selection to achieve variable selection with false discovery control for mixed data.

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