Unbiased Measurement of Feature Importance in Tree-Based Methods

Zhengze Zhou; Giles Hooker

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arXiv · 1903.05179

Unbiased Measurement of Feature Importance in Tree-Based Methods

Zhengze Zhou·Giles Hooker

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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.

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Zhengze Zhou, Giles Hooker. 2020-03-23. Unbiased Measurement of Feature Importance in Tree-Based Methods. https://arxiv.org/abs/1903.05179

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