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Unbiased Measurement of Feature Importance in Tree-Based Methods

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arxiv 1903.05179 v2 pith:OUPDGXY2 submitted 2019-03-12 stat.ML cs.LG

Zhengze Zhou , Giles Hooker This is my paper · ORCID
classification stat.MLcs.LG
keywords importancemethodssplit-improvementtree-basedappropriatelybeenbetterbias
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 17 citations worldwide. Full citation record

  1. Provable Recovery of Locally Important Signed Features and Interactions from Random Forest

    stat.ML 2025-12 conditional novelty 6.0 of 10

    Under a Locally Spike Sparse model and idealized Random Forest assumptions, LocalLSSFind provably recovers the signed features and interactions driving a single test prediction.

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