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A random forest guided tour

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 setti... Full description

Journal Title: Test (Madrid Spain), 2016-04-19, Vol.25 (2), p.197-227
Main Author: Biau, Gérard
Other Authors: Scornet, Erwan
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
ID: ISSN: 1133-0686
Link: https://hal.sorbonne-universite.fr/hal-01307105
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recordid: cdi_springer_journals_10_1007_s11749_016_0481_7
title: A random forest guided tour
format: Article
creator:
  • Biau, Gérard
  • Scornet, Erwan
subjects:
  • Aggregates
  • Algorithms
  • Classification
  • Datasets
  • Decision trees
  • Distance learning
  • Economics
  • Finance
  • general
  • Insurance
  • Invited Paper
  • Learning
  • Machine Learning
  • Management
  • Mathematical analysis
  • Mathematics
  • Mathematics and Statistics
  • om forests
  • omization
  • Parameter tuning
  • Regression
  • Regression analysis
  • Resampling
  • Statistical Theory and Methods
  • Statistics
  • Statistics for Business
  • Statistics Theory
  • Studies
  • Survival analysis
  • Tasks
  • Variable importance
ispartof: Test (Madrid, Spain), 2016-04-19, Vol.25 (2), p.197-227
description: 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.
language: eng
source:
identifier: ISSN: 1133-0686
fulltext: no_fulltext
issn:
  • 1133-0686
  • 1863-8260
url: Link


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descriptionThe 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.
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subjectAggregates ; Algorithms ; Classification ; Datasets ; Decision trees ; Distance learning ; Economics ; Finance ; general ; Insurance ; Invited Paper ; Learning ; Machine Learning ; Management ; Mathematical analysis ; Mathematics ; Mathematics and Statistics ; om forests ; omization ; Parameter tuning ; Regression ; Regression analysis ; Resampling ; Statistical Theory and Methods ; Statistics ; Statistics for Business ; Statistics Theory ; Studies ; Survival analysis ; Tasks ; Variable importance
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abstractThe 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.
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