ada: An R Package for Stochastic Boosting

Mark Culp, Kjell Johnson and George Michailides

Journal of Statistical Software, 2006, vol. 017, issue i02

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.

Date: 2006-09-26
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:017:i02

DOI: 10.18637/jss.v017.i02

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