README.md

In bagRboostR: Ensemble bagging and boosting classifiers

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

bagRboostR documentation built on May 2, 2019, 11:12 a.m.