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-4.7-arm64)fastAdaboost_1.0.0.zip(r-4.6-x86_64)fastAdaboost_1.0.0.zip(r-4.6-arm64)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

3.94 score 12 stars 146 scripts 37 downloads 1 mentions 3 exports 2 dependencies

Last updated from:f331ff8ccf. Checks:15 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK90
linux-devel-x86_64OK119
source / vignettesOK170
linux-release-arm64OK84
linux-release-x86_64OK111
macos-release-arm64OK89
macos-release-x86_64OK299
macos-oldrel-arm64OK77
macos-oldrel-x86_64OK210
windows-devel-arm64OK417
windows-devel-x86_64OK137
windows-release-arm64OK341
windows-release-x86_64OK97
windows-oldrel-x86_64OK103
wasm-releaseOK101

Exports:adaboostget_treereal_adaboost

Dependencies:Rcpprpart