What is concept drift in supervised learning systems?

Concept drift occurs when statistical properties of target variables change over time.

In real-world systems, data distributions evolve. Concept drift means the relationship between input features and output labels changes. This degrades model performance over time. Solutions include retraining, online learning, and drift detection methods.