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.


bagRboostR

bagRboostR is an R package consisting of 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.

Installation:

Via CRAN:

install.packages("bagRboostR")

Via Github:

library(devtools)

install_github("bagRboostR", username="shannonrush")

Future Features:

  • Additional weak classifiers

  • Support for regression

News

bagRboostR 0.0.2

  • Adding LICENSE file for MIT license

Reference manual

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install.packages("bagRboostR")

0.0.2 by Shannon Rush, 5 years ago


Browse source code at https://github.com/cran/bagRboostR


Authors: Shannon Rush <[email protected]>


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports randomForest

Suggests testthat


See at CRAN