Concept Drift

Concept drift refers to the phenomenon where the statistical properties of a target variable change over time in a predictive model. This change can occur due to various factors, such as evolving consumer behavior, market dynamics, or changes in underlying data distribution. As a result, models that were once accurate may become less effective or completely obsolete if they are not updated or retrained. Common use cases for addressing concept drift include financial forecasting, fraud detection, and recommendation systems, where timely adaptations to changing data patterns are crucial for maintaining performance.