Implements Adaboost based on C++ backend code. This is blazingly fast and especially useful for large, in memory data sets. The package uses decision trees as weak classifiers. Once the classifiers have been trained, they can be used to predict new data. Currently, we support only binary classification tasks. The package implements the Adaboost.M1 algorithm and the real Adaboost(SAMME.R) algorithm.

Documentation

Manual: fastAdaboost.pdf
Vignette: None available.

Maintainer: Sourav Chatterjee <souravc83 at gmail.com>

Author(s): Sourav Chatterjee*

Install package and any missing dependencies by running this line in your R console:

install.packages("fastAdaboost")

Depends R (>= 3.1.2)
Imports Rcpp, rpart
Suggests testthat, knitr, MASS
Enhances
Linking to Rcpp(>=0.12.0)
Reverse
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Package fastAdaboost
Materials
URL https://github.com/souravc83/fastAdaboost
Task Views
Version 1.0.0
Published 2016-02-28
License MIT + file LICENSE
BugReports https://github.com/souravc83/fastAdaboost/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks fastAdaboost check results
Package source fastAdaboost_1.0.0.tar.gz