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Boosted regression (boosting): An introductory tutorial and a Stata plugin

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  • Matthias Schonlau

    (RAND)

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    Abstract

    Boosting, or boosted regression, is a recent data-mining technique that has shown considerable success in predictive accuracy. This article gives an overview of boosting and introduces a new Stata command, boost, that im- plements the boosting algorithm described in Hastie, Tibshirani, and Friedman (2001, 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regression example, the R2 value computed on a test dataset is R2 = 21.3% for linear regression and R2 = 93.8% for boosting. In the logistic regression example, stepwise logistic regression correctly classifies 54.1% of the observations in a test dataset versus 76.0% for boosted logistic regression. Currently, boost accommodates Gaussian (normal), logistic, and Poisson boosted regression. boost is implemented as a Windows C++ plugin. Copyright 2005 by StataCorp LP.

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    Bibliographic Info

    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 5 (2005)
    Issue (Month): 3 (September)
    Pages: 330-354

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    Handle: RePEc:tsj:stataj:v:5:y:2005:i:3:p:330-354

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    Related research

    Keywords: boost; boosted regression; boosting; data mining;

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