CRAN/E | fastAdaboost

fastAdaboost

a Fast Implementation of Adaboost

Installation

About

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.

github.com/souravc83/fastAdaboost
Bug report File report

Key Metrics

Version 1.0.0
R ≥ 3.1.2
Published 2016-02-28 2832 days ago
Needs compilation? yes
License MIT
License File
CRAN checks fastAdaboost results

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Maintainer

Maintainer

Sourav Chatterjee

souravc83@gmail.com

Authors

Sourav Chatterjee

aut / cre

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.1.2

Imports

Rcpp
rpart

Suggests

testthat
knitr
MASS

LinkingTo

Rcpp ≥ 0.12.0

Reverse Depends

fasi