Concept drift describes the situation where the statistical characteristics of a data stream evolve over time, causing a discrepancy between the trained model and the current data distribution. This phenomenon can arise through multiple channels. For example, it may result from the emergence of new influencing factors, alterations in the significance of existing factors, or modifications to the interrelationships among factors. As a consequence, the model's performance may degrade significantly, necessitating adjustments or retraining to adapt to the new data patterns.
