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

3.95 score 12 stars 150 scripts 54 downloads 1 mentions 3 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK114
linux-devel-x86_64OK152
source / vignettesOK163
linux-release-arm64OK107
linux-release-x86_64OK131
macos-release-arm64OK127
macos-release-x86_64OK347
macos-oldrel-arm64OK88
macos-oldrel-x86_64OK312
windows-devel-arm64OK184
windows-devel-x86_64OK141
windows-release-arm64OK214
windows-release-x86_64OK91
windows-oldrel-x86_64OK95
wasm-releaseOK88

Exports:adaboostget_treereal_adaboost

Dependencies:Rcpprpart