Which of the following describes concept drift?

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc References:

Concept drift - Wikipedia

A Gentle Introduction to Concept Drift in Machine Learning

Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.

Data Drift vs. Concept Drift: What Is the Difference?