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.