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


  • Matthias Schonlau



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.

Suggested Citation

  • Matthias Schonlau, 2005. "Boosted regression (boosting): An introductory tutorial and a Stata plugin," Stata Journal, StataCorp LP, vol. 5(3), pages 330-354, September.
  • Handle: RePEc:tsj:stataj:v:5:y:2005:i:3:p:330-354

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    Cited by:

    1. Joyce P Jacobsen & Laurence M Levin & Zachary Tausanovitch, 2016. "Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 42(3), pages 387-398, June.
    2. Mosca, Irene & McCrory, Cathal, 2016. "Personality and wealth accumulation among older couples: Do dispositional characteristics pay dividends?," Journal of Economic Psychology, Elsevier, vol. 56(C), pages 1-19.
    3. Irene Mosca & Alan Barrett, 2016. "The impact of adult child emigration on the mental health of older parents," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(3), pages 687-719, July.
    4. Christoph Emanuel Mueller, 2016. "Accurate forecast of countries’ research output by macro-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1307-1328, November.
    5. Chuan Ding & Donggen Wang & Xiaolei Ma & Haiying Li, 2016. "Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-16, October.

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    boost; boosted regression; boosting; data mining;


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