ada
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.
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References in zbMATH (referenced in 17 articles , 1 standard article )
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- Esteban Alfaro; Matias Gamez; Noelia García: adabag: An R Package for Classification with Boosting and Bagging (2013) not zbMATH
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- Max Kuhn: Building Predictive Models in R Using the caret Package (2008) not zbMATH
- Mark Culp; Kjell Johnson; George Michailides: ada: An R Package for Stochastic Boosting (2006) not zbMATH
- Solaeche Galera, María Cristina: Lady Ada Byron and the first program for computers (1994)