Package: fastAdaboost 1.0.0

Sourav Chatterjee

fastAdaboost: 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.

Authors:Sourav Chatterjee [aut, cre]

fastAdaboost_1.0.0.tar.gz
fastAdaboost_1.0.0.zip(r-4.7-x86_64)fastAdaboost_1.0.0.zip(r-4.6-x86_64)fastAdaboost_1.0.0.zip(r-4.5-x86_64)
fastAdaboost_1.0.0.tgz(r-4.6-x86_64)fastAdaboost_1.0.0.tgz(r-4.6-arm64)fastAdaboost_1.0.0.tgz(r-4.5-x86_64)fastAdaboost_1.0.0.tgz(r-4.5-arm64)
fastAdaboost_1.0.0.tar.gz(r-4.7-arm64)fastAdaboost_1.0.0.tar.gz(r-4.7-x86_64)fastAdaboost_1.0.0.tar.gz(r-4.6-arm64)fastAdaboost_1.0.0.tar.gz(r-4.6-x86_64)
fastAdaboost_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
fastAdaboost/json (API)

# Install 'fastAdaboost' in R:
install.packages('fastAdaboost', repos = c('https://rickhelmus.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/souravc83/fastadaboost/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

3.93 score 11 stars 156 scripts 14 downloads 1 mentions 3 exports 2 dependencies

Last updated from:f331ff8ccf. Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK109
linux-devel-x86_64OK131
source / vignettesOK176
linux-release-arm64OK151
linux-release-x86_64OK126
macos-release-arm64OK98
macos-release-x86_64OK226
macos-oldrel-arm64OK100
macos-oldrel-x86_64OK201
windows-devel-x86_64OK92
windows-release-x86_64OK95
windows-oldrel-x86_64OK104
wasm-releaseOK104

Exports:adaboostget_treereal_adaboost

Dependencies:Rcpprpart