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Unbiased variable importance for random forests

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  • Markus Loecher

Abstract

The default variable-importance measure in random forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting criterion. While the alternative permutation importance is generally accepted as a reliable measure of variable importance, it is also computationally demanding and suffers from other shortcomings. We propose a simple solution to the misleading/untrustworthy Gini importance which can be viewed as an over-fitting problem: we compute the loss reduction on the out-of-bag instead of the in-bag training samples.

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  • Markus Loecher, 2022. "Unbiased variable importance for random forests," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(5), pages 1413-1425, March.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:5:p:1413-1425
    DOI: 10.1080/03610926.2020.1764042
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    Cited by:

    1. Zhigang Li & Jie Yang & Jialong Zhong & Dong Zhang, 2022. "Assessment of Urban Agglomeration Ecological Sustainability and Identification of Influencing Factors: Based on the 3DEF Model and the Random Forest," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    2. Hapfelmeier, Alexander & Hornung, Roman & Haller, Bernhard, 2023. "Efficient permutation testing of variable importance measures by the example of random forests," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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