bagRboostR: Ensemble bagging and boosting classifiers

bagRboostR is a set of ensemble classifiers for multinomial classification. The bagging function is the implementation of Breiman's ensemble as described by Opitz & Maclin (1999). The boosting function is the implementation of Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging and SAMME implementations use randomForest as the weak classifier and expect a character outcome variable. Each ensemble classifier returns a character vector of predictions for the test set.

Getting started

Package details

AuthorShannon Rush <>
MaintainerShannon Rush <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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bagRboostR documentation built on May 2, 2019, 11:12 a.m.