souravc83/fastBoost: a fast implementation of adaboost.M1 and real adaboost

This library implements a Rcpp based blazingly fast implementation of adaboost.m1 and real adaboost. This will be especially well suited for big datasets. The library currently supports decision trees as the weak classifier. Once the classifiers have been trained, they can be used to predict new datasets. Currently, we support only binary classification task. In addition to calculating the final error, a staged error is also calculated for each additional tree. This can be used to tune the final number of iterations. A plot of the staged error is also generated to help the user decide.

Getting started

Package details

Authorperson("Sourav", "Chatterjee", , "souravc83@gmail.com", c("aut", "cre"))
MaintainerSourav Chatterjee <souravc83@gmail.com>
LicenseMIT + file LICENSE
Version1.0
URL https://github.com/souravc83/fastBoost
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("souravc83/fastBoost")
souravc83/fastBoost documentation built on May 30, 2019, 6:34 a.m.