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.”

For example, customer purchasing behavior over time that may be influenced by the strength of the economy, where the strength of the economy is not explicitly specified in the data. These elements are also called a “hidden context”.

Last Updated: April 07, 2019