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

2026-09-12

AI Summary

本文解决了树基方法中变量重要性评分偏向连续预测变量的问题，通过向分类预测变量添加少量噪声的方法进行修正。

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.

Problem

• variable importance
• tree-based methods
• bias
• mixed data

Innovation

• noise
• variable importance
• tree-based methods
• bias correction
• mixed data