Adaptive Machine Learning Feature Selection Framework

Design a comprehensive feature selection pipeline that dynamically adapts to changing data distributions using advanced machine learning techniques. Implement a multi-stage approach incorporating mutual information, recursive feature elimination, and regularization techniques like Lasso and ElasticNet. Develop an automated system that can handle high-dimensional datasets, perform feature importance ranking, and detect concept drift. Include mechanisms for feature engineering, statistical significance testing, and model interpretability scoring.