Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

An Introduction to Ensemble Methods for Data Analysis

Bibliographic Data

ID2330489
AuthorsRichard A Berk (0000-0002-2983-1276, University of California, Los Angeles, corresponding author)
Year2006
Volume34
Issue3
Pages263-295
Publication date2006-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methods & Research (JOURNAL)
Journal identifiersISSN: 0049-1241 • E-ISSN: 1552-8294
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/0049124105283119
OpenAlexW1537637778
LanguageEN
Citations received14
References cited18

This article provides an introduction to ensemble statistical procedures as a special case of algorithmic methods. The discussion begins with classification and regression trees (CART) as a didactic device to introduce many of the key issues. Following the material on CART is a consideration of cross-validation, bagging, random forests, and boosting. Major points are illustrated with analyses of real data

Computer security · Data mining · Data science · Function (biology) · Key (lock) · Machine learning · Prison · Anomaly Detection Techniques and Applications · Artificial Intelligence · Computer Science · Face and Expression Recognition · Machine Learning and Data Classification · Psychology

  • If I Had a Hammer, I Would Not Use it to Control Drunk Driving

    Open Access•Matthew Demichele, Brian K Payne•Criminology & Public Policy•2013

  • Research Designs for Program Evaluation

    Open Access•Vivian C Wong, Coady Wing et al.•Handbook of Psychology, Second…•2012

  • Improving propensity score weighting using machine learning

    Open Access•Brian K Lee, Justin Lessler et al.•Statistics in Medicine•2010

  • Big Data in Industrial-Organizational Psychology and Human Resource Management

    Frederick L Oswald, Tara S Behrend et al.•Annual Review of Organizational…•2019

  • Harnessing heterogeneity in behavioural research using computational social science

    Open Access•Giuseppe Veltri•Behavioural Public Policy•2023

  • Decision Trees and Random Forests

    Open Access•Simon Hegelich•European Policy Analysis•2016

  • The most salient global predictors of adolescents’ subjective Well-Being

    Open Access•Yi-Jhen Wu, Jihyun Lee•Child Indicators Research•2022

  • Use Probation to Prevent Murder

    Open Access•Lawrence W Sherman•Criminology & Public Policy•2007

  • Perceptions of Competence and the European Economic Crisis

    Open Access•Giacomo Chiozza, Luigi Manzetti•Political Research Quarterly•2015

  • Trees and forest. Recursive partitioning as an alternative to parametric regression models in social sciences

    Open Access•Nicolas Robette•Bulletin of Sociological…•2022

  • Race and the Decision to Seek the Death Penalty in Federal Cases

    Stephen P Klein, Richard A Berk et al.•Race and the decision to seek the…•2006

  • School climate and student-based contextual learning factors as predictors of school absenteeism severity at multiple levels via CHAID analysis

    Open Access•Victoria R Bacon, Victoria Bacon et al.•Children and Youth Services Review•2020

  • What Tears Couples Apart

    Open Access•Bruno Arpino, Marco Le Moglie et al.•Demography•2022

  • Big Data is not only about data

    Open Access•Giuseppe Alessandro Veltri, Giuseppe Veltri•Big Data & Society•2017

  • Observational Studies

    Open Access•Paul R Rosenbaum•Observational Studies•2002

  • Stochastic gradient boosting

    Open Access•Jerome H Friedman•Computational Statistics & Data…•2002

  • Additive logistic regression

    Jerome Friedman, Jerome H Friedman et al.•The Annals of Statistics•2000

  • Bagging predictors

    Open Access•Leo Breiman•Machine Learning•1996

  • Random Forests

    Open Access•Leo Breiman•Machine Learning•2001

Unique citing works14
Citations per year0,7
Citation span2006 - 2023 (18)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 13

Tools

Open DOISci-HubOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae