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)fastAdaboost_1.0.0.zip(r-4.6)fastAdaboost_1.0.0.zip(r-4.5)
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
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

3.90 score 11 stars 146 scripts 17 downloads 1 mentions 3 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK118
linux-devel-x86_64OK123
source / vignettesOK172
linux-release-arm64OK115
linux-release-x86_64OK117
macos-release-arm64OK128
macos-release-x86_64OK207
macos-oldrel-arm64OK104
macos-oldrel-x86_64OK173
windows-develOK100
windows-releaseOK101
windows-oldrelOK100
wasm-releaseOK97

Exports:adaboostget_treereal_adaboost

Dependencies:Rcpprpart