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An Introduction to Ensemble Methods for Data Analysis

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  • Richard A. Berk

    (University of California, Los Angeles)

Abstract

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.

Suggested Citation

  • Richard A. Berk, 2006. "An Introduction to Ensemble Methods for Data Analysis," Sociological Methods & Research, , vol. 34(3), pages 263-295, February.
    • Handle: RePEc:sae:somere:v:34:y:2006:i:3:p:263-295
    DOI: 10.1177/0049124105283119
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    References listed on IDEAS

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    1. Mojirsheibani, M., 1997. "A consistent combined classification rule," Statistics & Probability Letters, Elsevier, vol. 36(1), pages 43-47, November.
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    Cited by:

    1. Zhengyang Dong, 2018. "Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies," Papers 1805.12111, arXiv.org, revised Feb 2019.
    2. Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
    3. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
    4. Cleridy E. Lennert‐Cody & Richard A. Berk, 2007. "Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 671-689, July.

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