Dynamic Concept Drift Detection and Adaptation System

Develop a comprehensive concept drift detection framework that can automatically identify, characterize, and adapt to changing data distributions in real-time. Implement multiple drift detection algorithms including statistical tests, density-based methods, and machine learning approaches. Create a modular system that supports automatic model retraining, feature space adaptation, and performance monitoring. Include comprehensive logging, visualization of drift characteristics, and automated alerting mechanisms.