Package: ensembleML 0.2.5

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>.

Authors:Sadikul Islam [aut, cre]