A Random Forest Guided Tour

Abstract : The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is versatile enough to be applied to large-scale problems, is easily adapted to various ad-hoc learning tasks, and returns measures of variable importance. The present article reviews the most recent theoretical and methodological developments for random forests. Emphasis is placed on the mathematical forces driving the algorithm, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures. This review is intended to provide non-experts easy access to the main ideas.
Type de document :
Pré-publication, Document de travail
2015
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01221748
Contributeur : Erwan Scornet <>
Soumis le : mercredi 28 octobre 2015 - 14:54:24
Dernière modification le : jeudi 22 novembre 2018 - 14:09:17
Document(s) archivé(s) le : vendredi 28 avril 2017 - 07:42:08

Fichiers

article2.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01221748, version 1
  • ARXIV : 1511.05741

Collections

Relations

Citation

Gérard Biau, Erwan Scornet. A Random Forest Guided Tour. 2015. 〈hal-01221748〉

Partager

Métriques

Consultations de la notice

176

Téléchargements de fichiers

56