Implement model explainability using SHAP, including global importance, summary plots, and single-row waterfalls. Add abstract base model class for model wrappers, create a model zoo for various estimators based on dataset size and task type, and develop a cross-validation trainer for model evaluation. Integrate MLflow for experiment tracking, logging dataset stats, model results, and SHAP plots. Introduce Optuna-based hyperparameter tuning for top models from the leaderboard, with parameter suggestion spaces for various algorithms.
