Unbiased Measurement of Feature Importance in Tree-Based Methods
2019-03-12Code Available0· sign in to hype
Zhengze Zhou, Giles Hooker
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/ZhengzeZhou/unbiased-feature-importanceOfficialIn papernone★ 0
Abstract
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as measured on out of sample data, this bias can be corrected yielding better summaries and screening tools.