TY - JOUR AU - Culp, Mark AU - Johnson, Kjell AU - Michailides, George PY - 2006/09/26 Y2 - 2024/03/28 TI - ada: An R Package for Stochastic Boosting JF - Journal of Statistical Software JA - J. Stat. Soft. VL - 17 IS - 2 SE - Articles DO - 10.18637/jss.v017.i02 UR - https://www.jstatsoft.org/index.php/jss/article/view/v017i02 SP - 1 - 27 AB - 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. ER -