Concept Drift
A change in the underlying relationship between inputs and outputs over time. Unlike data drift, concept drift means the rules of the game have changed, not just the distribution of inputs.
Why It Matters
Concept drift is harder to detect than data drift because the inputs may look the same but the correct predictions have changed. It requires rethinking, not just retraining.
Example
A spam filter trained to flag emails with 'free offer' becoming less effective as spammers evolve tactics and legitimate marketers start using similar language.
Think of it like...
Like tax law changing — even if people's incomes stay the same (no data drift), the correct tax calculation is different because the rules themselves changed.
Related Terms
Data Drift
A change in the statistical properties of the input data over time compared to the data the model was trained on. When data drifts, model predictions become less reliable.
Model Monitoring
The practice of continuously tracking an ML model's performance, predictions, and input data in production to detect degradation, drift, or anomalies after deployment.
Retraining
The process of training a model again on updated data to restore or improve its performance. Retraining addresses model drift and incorporates new patterns the original model did not learn.