Machine Learning with AdaBoost on Decision Stumps [R package sboost version 0.1.2]

Jadon Wagstaff

sboost: Machine Learning with AdaBoost on Decision Stumps

Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.

Version: 0.1.2

Depends: R (≥ 3.4.0)

Imports: dplyr (≥ 0.7.6), rlang (≥ 0.2.1), Rcpp (≥ 0.12.17), stats (≥ 3.4)

LinkingTo: Rcpp (≥ 0.12.17)

Suggests: testthat

Published: 2022年05月26日

DOI: 10.32614/CRAN.package.sboost

Author: Jadon Wagstaff [aut, cre]

Maintainer: Jadon Wagstaff <jadonw at gmail.com>

BugReports: https://github.com/jadonwagstaff/sboost/issues

License: MIT + file LICENSE

URL: https://github.com/jadonwagstaff/sboost

NeedsCompilation: yes

Materials: README, NEWS

CRAN checks: sboost results

Documentation:

Reference manual: sboost.html , sboost.pdf

Downloads:

Package source: sboost_0.1.2.tar.gz

Windows binaries: r-devel: sboost_0.1.2.zip, r-release: sboost_0.1.2.zip, r-oldrel: sboost_0.1.2.zip

macOS binaries: r-release (arm64): sboost_0.1.2.tgz, r-oldrel (arm64): sboost_0.1.2.tgz, r-release (x86_64): sboost_0.1.2.tgz, r-oldrel (x86_64): sboost_0.1.2.tgz

Old sources: sboost archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sboost to link to this page.