Models under which random forests perform badly; consequences for applications

DOI10.1007/s00180-021-01182-4zbMath1505.62143arXiv1910.00943OpenAlexW3216426195MaRDI QIDQ2095717

Yanyan Li

Publication date: 15 November 2022

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.00943

zbMATH Keywords

convergenceconsistencyrandom forestsstatistical prediction

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• rpart
• randomForest

Cites Work

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• Some models and methods for the analysis of observational data
• Consistency of random survival forests
• Distribution-free consistency results in nonparametric discrimination and regression function estimation
• Consistent nonparametric regression. Discussion
• A distribution-free theory of nonparametric regression
• Consistency of random forests
• A random forest guided tour
• Impact of subsampling and tree depth on random forests
• Reinforcement Learning Trees
• Random forests

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