Analysis of a Random Forests Model

被引：0

作者：

Biau, Gerard ^{[1
,2
]}

机构：

[1] Univ Paris 06, LSTA & LPMA, F-75252 Paris 05, France

[2] Ecole Normale Super, DMA, F-75230 Paris 05, France

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2012年 / 13卷

关键词：

random forests; randomization; sparsity; dimension reduction; consistency; rate of convergence; REGRESSION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 (2004), 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.

引用

页码：1063 / 1095

页数：33

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