Analysis of a Random Forests Model

Abstract : Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this paper, we offer an in-depth analysis of a random forests model suggested by Breiman in \cite{Bre04}, which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
Type de document :
Pré-publication, Document de travail
2010
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https://hal.archives-ouvertes.fr/hal-00476545
Contributeur : Gérard Biau <>
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Dernière modification le : lundi 29 mai 2017 - 14:23:06
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  • HAL Id : hal-00476545, version 3
  • ARXIV : 1005.0208

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INSMI | UPMC | LSTA | PSL | USPC | PMA

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Gérard Biau. Analysis of a Random Forests Model. 2010. 〈hal-00476545v3〉

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