What is concept drift and how is it handled?

Concept drift occurs when the statistical properties of the target variable (the concept being predicted) change over time, causing a deployed model to degrade. Types: Gradual drift (slow change over time), Sudden drift (abrupt change, e.g., COVID-19 changing user behavior), Recurring drift (cyclical patterns). Detection methods: monitoring model performance metrics, population stability index (PSI), ADWIN, and Page-Hinkley test. Mitigation: periodic retraining, sliding window approaches, ensemble methods that weight recent data, and continuous learning/online learning systems.