Impact of subsampling and tree depth on random forests

DOI10.1051/PS/2018008zbMath1409.62072arXiv1603.04261OpenAlexW2794159684MaRDI QIDQ4615432

Roxane Duroux, Erwan Scornet

Publication date: 28 January 2019

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1603.04261

zbMATH Keywords

randomizationdecision treesrandom forestssubsamplingparameter tuningtree depth

Mathematics Subject Classification ID

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)

Related Items (4)

Large Scale Prediction with Decision Trees ⋮ Tuning parameters in random forests ⋮ Unnamed Item ⋮ Models under which random forests perform badly; consequences for applications

Uses Software

• SuperLearner
• GeneSrF

Cites Work

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• On the asymptotics of random forests
• Handbook of computational statistics. Concepts and methods.
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• High-dimensional additive modeling
• Optimal rates of convergence for nonparametric estimators
• Optimal global rates of convergence for nonparametric regression
• Consistency of random forests
• Comments on: ``A random forest guided tour
• Cellular Tree Classifiers
• Variance reduction in purely random forests
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Super Learner
• Random forests

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