Conceptual Graphs
Machine Learning
The phenomenon where the relationship between input features and target variables changes over time.
Detailed Explanation
Concept Drift refers to the change in the statistical relationship between input features and target variables over time. This phenomenon can lead to degraded model performance, as the patterns learned during training no longer accurately represent current data. Detecting and adapting to concept drift is essential for maintaining the accuracy and reliability of machine learning models in dynamic environments.
Use Cases
- •Use case: Continuously monitor streaming data to detect and update models, maintaining accuracy despite changing data patterns over time.
Related Terms
Other terms in the Machine Learning category