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: | 2022-05-26 |
| 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 |
| Reference manual: | sboost.pdf |
| 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 |
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