Definition
Concept drift is a phenomenon in machine learning where the relationship between input features and the target output changes as new data arrives. This often happens in dynamic environments where user behavior or underlying physical processes evolve. If a model is not updated or adapted to these changes, its predictive accuracy will decline. Detecting and handling concept drift is essential for maintaining robust performance in production systems, requiring techniques like retraining or online learning.
Summary
Concept drift occurs when the statistical properties of the target variable change over time, degrading model performance.
Key Concepts
- Data Distribution Shift
- Model Degradation
- Online Learning
- Retraining
Use Cases
- Fraud detection systems
- Stock market prediction
- Customer churn modeling