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.

AuthorShannon Rush <shannonmrush@gmail.com>
Date of publication2014-03-05 18:13:45
MaintainerShannon Rush <shannonmrush@gmail.com>
LicenseMIT + file LICENSE
Version0.0.2

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Files

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

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