Machine Learning with AdaBoost on Decision Stumps R package sboost version 0.1.2 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. Software 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 Jadon Wagstaff <jadonw@gmail.com> Comprehensive R Archive Network (CRAN) Jadon Wagstaff [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=sboost/LICENSE) 2022-05-26 application/tgz https://CRAN.R-project.org/package=sboost doi:10.32614/CRAN.package.sboost