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• W1799405941 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." @default.
• W1799405941 created "2016-06-24" @default.
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• W1799405941 date "2006-01-01" @default.
• W1799405941 modified "2023-10-14" @default.
• W1799405941 title "<b>ada</b>: An<i>R</i>Package for Stochastic Boosting" @default.
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• W1799405941 doi "https://doi.org/10.18637/jss.v017.i02" @default.
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