bagRboostR (0.0.2)

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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.

Maintainer: Shannon Rush
Author(s): Shannon Rush <>

License: MIT + file LICENSE

Uses: randomForest, testthat

Released about 4 years ago.

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