fastAdaboost (1.0.0)

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a Fast Implementation of Adaboost.

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.

Maintainer: Sourav Chatterjee
Author(s): Sourav Chatterjee [aut, cre]

License: MIT + file LICENSE

Uses: Rcpp, rpart, MASS, testthat, knitr

Released about 2 years ago.



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