Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Mark Culp, Kjell Johnson, George Michailides
Title: ada: An R Package for Stochastic Boosting
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.

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Paper: ada: An R Package for Stochastic Boosting     Download PDF (Downloads: 11690)
Supplements:
ada_2.0-1.tar.gz: R source code Download (Downloads: 1358; 916KB)
v17i02.R.zip: v17i02.R: R example code from the paper Download (Downloads: 1403; 1KB)
DOI: 10.18637/jss.v017.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.