Creates classifier for binary outcomes using Freund and Schapire's Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 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.
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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