A random forest guided tour

Gérard Biau () and Erwan Scornet ()
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Gérard Biau: Sorbonne Universités, UPMC Univ Paris 06, CNRS, Laboratoire de Statistique Théorique et Appliquées (LSTA)
Erwan Scornet: Sorbonne Universités, UPMC Univ Paris 06, CNRS, Laboratoire de Statistique Théorique et Appliquées (LSTA)

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 2, No 1, 197-227

Abstract: 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.

Keywords: Random forests; Randomization; Resampling; Parameter tuning; Variable importance; 62G02 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (188)

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DOI: 10.1007/s11749-016-0481-7

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