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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | sboost_0.1.2.tar.gz |
1.0 MiB |
0.1.2 |
rolling linux/noble R-4.5 | sboost_0.1.2.tar.gz |
1.0 MiB |
0.1.2 |
rolling source/ R- | sboost_0.1.2.tar.gz |
730.7 KiB |
0.1.2 |
latest linux/jammy R-4.5 | sboost_0.1.2.tar.gz |
1.0 MiB |
0.1.2 |
latest linux/noble R-4.5 | sboost_0.1.2.tar.gz |
1.0 MiB |
0.1.2 |
latest source/ R- | sboost_0.1.2.tar.gz |
730.7 KiB |
0.1.2 |
2026-04-26 source/ R- | sboost_0.1.2.tar.gz |
730.7 KiB |
0.1.2 |
2026-04-23 source/ R- | sboost_0.1.2.tar.gz |
730.7 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | sboost_0.1.2.zip |
1.3 MiB |
0.1.2 |
2025-04-20 source/ R- | sboost_0.1.2.tar.gz |
730.7 KiB |