IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v5y2005i3p330-354.html
   My bibliography  Save this article

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

Author

Listed:
  • Matthias Schonlau

    (RAND)

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.

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
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0087
    Download Restriction: no
    File URL: http://www.stata-journal.com/software/sj5-3/st0087/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    2. 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.
    3. 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.
    4. Yiyi Chen & Ye Liu, 2021. "Which Risk Factors Matter More for Psychological Distress during the COVID-19 Pandemic? An Application Approach of Gradient Boosting Decision Trees," IJERPH, MDPI, vol. 18(11), pages 1-18, May.
    5. 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.
    6. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    7. 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.
    8. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    9. Dennis W. Campbell & Ruidi Shang, 2022. "Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews," Management Science, INFORMS, vol. 68(9), pages 7034-7053, September.
    10. Frenger, Monika & Emrich, Eike & Geber, Sebastian & Follert, Florian & Pierdzioch, Christian, 2019. "The influence of performance parameters on market value," Working Papers of the European Institute for Socioeconomics 30, European Institute for Socioeconomics (EIS), Saarbrücken.
    11. Yang, Jiawen & Su, Pinren & Cao, Jason, 2020. "On the importance of Shenzhen metro transit to land development and threshold effect," Transport Policy, Elsevier, vol. 99(C), pages 1-11.
    12. 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, vol. 8(11), pages 1-16, October.
    13. Joanna F Dipnall & Julie A Pasco & Michael Berk & Lana J Williams & Seetal Dodd & Felice N Jacka & Denny Meyer, 2016. "Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
    14. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
    15. Yang, Haoran & Zhang, Qinran & Helbich, Marco & Lu, Yi & He, Dongsheng & Ettema, Dick & Chen, Long, 2022. "Examining non-linear associations between built environments around workplace and adults’ walking behaviour in Shanghai, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 234-246.
    16. Barth, Michael & Emrich, Eike & Güllich, Arne, 2018. "A machine learning approach to 'revisit' specialization and sampling in institutionalized practice," Working Papers of the European Institute for Socioeconomics 26, European Institute for Socioeconomics (EIS), Saarbrücken.
    17. Zhesong Hao & Ying Peng, 2022. "Comparing Nonlinear and Threshold Effects of Bus Stop Proximity on Transit Use and Carbon Emissions in Developing Cities," Land, MDPI, vol. 12(1), pages 1-21, December.
    18. repec:ags:aaea22:335799 is not listed on IDEAS
    19. Michael Barth & Eike Emrich & Arne Güllich, 2019. "A Machine Learning Approach to “Revisit†Specialization and Sampling in Institutionalized Practice," SAGE Open, , vol. 9(2), pages 21582440198, April.
    20. Zou, Guojian & Lai, Ziliang & Li, Ye & Liu, Xinghua & Li, Wenxiang, 2022. "Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach," Economics of Transportation, Elsevier, vol. 31(C).
    21. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    22. Daniel Homocianu & Dinu Airinei, 2022. "PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets," Mathematics, MDPI, vol. 10(15), pages 1-27, July.
    23. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tsj:stataj:v:5:y:2005:i:3:p:330-354. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.