ensembleML
Unified Interface for Ensemble Machine Learning Methods
Provides a clean, unified interface for training, predicting, and evaluating ensemble machine learning models including Random Forest, Gradient Boosting ('XGBoost'), 'AdaBoost', and 'Bagging'. All algorithms share a consistent API: em_fit(), em_predict(), em_evaluate(), and em_tune(). Includes built-in cross-validation, feature importance, calibration diagnostics, partial dependence plots, and model comparison utilities. Methods: Breiman (2001) <doi:10.1023/A:1010933404324>; Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>; Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>; Breiman (1996) <doi:10.1007/BF00058655>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.5 |
rolling linux/jammy R-4.5 | ensembleML_0.2.5.tar.gz |
127.5 KiB |
0.2.5 |
rolling linux/noble R-4.5 | ensembleML_0.2.5.tar.gz |
127.5 KiB |
0.2.5 |
rolling source/ R- | ensembleML_0.2.5.tar.gz |
47.6 KiB |
0.2.5 |
latest linux/jammy R-4.5 | ensembleML_0.2.5.tar.gz |
127.5 KiB |
0.2.5 |
latest linux/noble R-4.5 | ensembleML_0.2.5.tar.gz |
127.5 KiB |
0.2.5 |
latest source/ R- | ensembleML_0.2.5.tar.gz |
47.6 KiB |
0.2.5 |
2026-04-23 source/ R- | ensembleML_0.2.5.tar.gz |
0 B |