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.

Author
Shannon Rush <shannonmrush@gmail.com>
Date of publication
2014-03-05 18:13:45
Maintainer
Shannon Rush <shannonmrush@gmail.com>
License
MIT + file LICENSE
Version
0.0.2

View on CRAN

Man pages

bagging
Ensemble bagging classifier for multinomial classification
samme
Ensemble boosting classifier for multinomial classification

Files in this package

bagRboostR
bagRboostR/inst
bagRboostR/inst/tests
bagRboostR/inst/tests/test_bagging.R
bagRboostR/inst/tests/test_helpers.R
bagRboostR/inst/tests/test_samme.R
bagRboostR/tests
bagRboostR/tests/test-all.R
bagRboostR/NAMESPACE
bagRboostR/NEWS
bagRboostR/R
bagRboostR/R/bagging.R
bagRboostR/R/helpers.R
bagRboostR/R/samme.R
bagRboostR/README.md
bagRboostR/MD5
bagRboostR/DESCRIPTION
bagRboostR/man
bagRboostR/man/bagging.Rd
bagRboostR/man/samme.Rd
bagRboostR/LICENSE