Random Forests: some methodological insights - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2008

Random Forests: some methodological insights

(1, 2) , (1, 2) , (3)
1
2
3

Abstract

This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both standard problems as well as high dimensional ones for which the number of variables hugely exceeds the sample size. But the main contribution of this paper is twofold: to provide some insights about the behavior of the variable importance index based on random forests and in addition, to propose to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The strategy involves a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.
Fichier principal
Vignette du fichier
RR-6729.pdf (452.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00340725 , version 1 (21-11-2008)

Identifiers

  • HAL Id : inria-00340725 , version 1
  • ARXIV : 0811.3619

Cite

Robin Genuer, Jean-Michel Poggi, Christine Tuleau. Random Forests: some methodological insights. [Research Report] RR-6729, INRIA. 2008. ⟨inria-00340725⟩
719 View
757 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More