Unbiased Measurement of Feature Importance in Tree-Based Methods

03/12/2019

∙

by   Zhengze Zhou, et al.

∙

0

∙

share

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.