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

AuthorJadon Wagstaff [aut, cre]
MaintainerJadon Wagstaff <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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sboost documentation built on May 2, 2019, 2:34 a.m.