SOTAVerified

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.

Reproduce

Code

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.

Tasks

Reproductions