|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.|
Page views:: 7854. Submitted: 2005-07-13. Published: 2006-09-26.
ada: An R Package for Stochastic Boosting
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