swMATH ID: 9505
Software Authors: George Michailides, Kjell Johnson, Mark Culp
Description: ada: An R Package for Stochastic Boosting. 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.
Homepage: https://cran.r-project.org/web/packages/ada/index.html
Source Code:  https://github.com/cran/ada
Dependencies: R
Related Software: R; rpart; gbm; caret; randomForest; mboost; e1071; Kernlab; ipred; nnet; MASS (R); UCI-ml; glmnet; C4.5; neuralnet; pls; lattice; mda; klaR; mlbench
Cited in: 13 Publications

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