Consistent and unbiased variable selection under indepedent features using random forest permutation importance

DOI10.3150/22-BEJ1534OpenAlexW4367320326WikidataQ123095402 ScholiaQ123095402MaRDI QIDQ6103229

Markus Pauly, Burim Ramosaj

Publication date: 2 June 2023

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/bernoulli/volume-29/issue-3/Consistent-and-unbiased-variable-selection-under-indepedent-features-using-Random/10.3150/22-BEJ1534.full

zbMATH Keywords

consistencyunbiasednessstatistical learningrandom forestpermutation importanceout-of-bag samples

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Multivariate analysis (62Hxx) Nonparametric inference (62Gxx)

Cites Work

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• Correlation and variable importance in random forests
• Empirical characterization of random forest variable importance measures
• Asymptotic properties of high-dimensional random forests
• Consistent estimation of residual variance with random forest out-of-bag errors
• Consistency of random forests
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• 10.1162/153244303322753616
• Reinforcement Learning Trees
• Random forests

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