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.
|Author||Jadon Wagstaff [aut, cre]|
|Maintainer||Jadon Wagstaff <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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