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.
- Version0.1.2
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?Yes
- Last release05/26/2022
Documentation
Team
Jadon Wagstaff
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- Imports3 packages
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