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
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 24 downloads 1 mentions 3 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK131
linux-devel-x86_64OK129
source / vignettesOK157
linux-release-arm64OK166
linux-release-x86_64OK119
macos-release-arm64OK106
macos-release-x86_64OK204
macos-oldrel-arm64OK80
macos-oldrel-x86_64OK267
windows-develOK99
windows-releaseOK99
windows-oldrelOK121
wasm-releaseOK126

Exports:adaboostget_treereal_adaboost

Dependencies:Rcpprpart