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.
| Version: | 0.0.2 |
| Imports: | randomForest |
| Suggests: | testthat |
| Published: | 2014-03-05 |
| Author: | Shannon Rush |
| Maintainer: | Shannon Rush <shannonmrush at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | bagRboostR results |
| Reference manual: | bagRboostR.pdf |
| Package source: | bagRboostR_0.0.2.tar.gz |
| Windows binaries: | r-devel: bagRboostR_0.0.2.zip, r-release: bagRboostR_0.0.2.zip, r-oldrel: bagRboostR_0.0.2.zip |
| OS X Mavericks binaries: | r-release: bagRboostR_0.0.2.tgz, r-oldrel: bagRboostR_0.0.2.tgz |
| Old sources: | bagRboostR archive |
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