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ada: An R Package for Stochastic Boosting

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  • Culp, Mark
  • Johnson, Kjell
  • Michailides, George

Abstract

Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.

Suggested Citation

  • Culp, Mark & Johnson, Kjell & Michailides, George, 2006. "ada: An R Package for Stochastic Boosting," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i02).
    • Handle: RePEc:jss:jstsof:v:017:i02
    DOI: http://hdl.handle.net/10.18637/jss.v017.i02
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Matthias Bogaert & Michel Ballings & Martijn Hosten & Dirk Van den Poel, 2017. "Identifying Soccer Players on Facebook Through Predictive Analytics," Decision Analysis, INFORMS, vol. 14(4), pages 274-297, December.
    2. Dominik P. Heinisch & Guido Buenstorf, 2018. "The next generation (plus one): an analysis of doctoral students’ academic fecundity based on a novel approach to advisor identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 351-380, October.
    3. Ying-Qi Zhao & Michael R. Kosorok, 2014. "Discussion of combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 713-716, September.
    4. Alfaro, Esteban & Gamez, Matias & García, Noelia, 2013. "adabag: An R Package for Classification with Boosting and Bagging," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i02).
    5. Heinisch, Dominik & Koenig, Johannes & Otto, Anne, 2019. "The IAB-INCHER project of earned doctorates (IIPED): A supervised machine learning approach to identify doctorate recipients in the German integrated employment biography data," IAB-Discussion Paper 201913, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    6. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.

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