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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.
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Package details |
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| Author | Jadon Wagstaff [aut, cre] |
| Maintainer | Jadon Wagstaff <jadonw@gmail.com> |
| License | MIT + file LICENSE |
| Version | 0.1.2 |
| URL | https://github.com/jadonwagstaff/sboost |
| Package repository | View on CRAN |
| Installation |
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