An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied succ...

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Bibliographic Details
Published in	Psychological methods Vol. 14; no. 4; pp. 323 - 348
Main Authors	Strobl, Carolin, Malley, James, Tutz, Gerhard
Format	Journal Article
Language	English
Published	United States American Psychological Association 01.12.2009
Subjects	Artificial Intelligence Classification Data Interpretation, Statistical Decision Making Genetics Humans Models, Psychological Multivariate Analysis Predictor Variables Psychological Studies Psychology - methods Psychology - statistics & numerical data Regression Analysis Research Methodology Statistical Analysis Statistics, Nonparametric
Online Access	Get full text
ISSN	1082-989X 1939-1463 1939-1463
DOI	10.1037/a0016973

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Abstract	Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (Contains 2 tables and 10 figures.)
AbstractList	Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (Contains 2 tables and 10 figures.) Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.
Author	Malley, James Tutz, Gerhard Strobl, Carolin
Author_xml	– sequence: 1 fullname: Strobl, Carolin – sequence: 2 fullname: Malley, James – sequence: 3 fullname: Tutz, Gerhard
BackLink	http://eric.ed.gov/ERICWebPortal/detail?accno=EJ865292$$DView record in ERIC https://www.ncbi.nlm.nih.gov/pubmed/19968396$$D View this record in MEDLINE/PubMed
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Snippet	Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially...
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SubjectTerms	Artificial Intelligence Classification Data Interpretation, Statistical Decision Making Genetics Humans Models, Psychological Multivariate Analysis Predictor Variables Psychological Studies Psychology - methods Psychology - statistics & numerical data Regression Analysis Research Methodology Statistical Analysis Statistics, Nonparametric
Title	An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests
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