What is Model drift?
A model getting worse over time because the real world no longer looks like its training data.
In plain English
Data drift means the inputs change, for example new kinds of customers. Concept drift means the relationship between inputs and the right answer changes, for example fraud tactics evolve. The remedy is monitoring: compare live performance and input data against a baseline, then retrain or update when it slips.
Example
A demand forecast trained before a major market change begins to miss badly.
Easy to confuse with
- Overfitting: Drift happens after deployment. Overfitting is a training problem.
Where you will meet it
This idea belongs to the subject area of these practice tests. We do not publish or predict real exam questions.
Related terms
- Overfitting: When a model learns its training data too closely and does badly on new data.
- Bias in AI: Results that are systematically unfair to some people or groups.
- Supervised vs unsupervised learning: Supervised learning trains on labelled examples. Unsupervised learning looks for structure in unlabelled data.
Test yourself
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