AI engineering glossary · ML fundamentals
What is active learning?
Active learning: A strategy in which the algorithm chooses which unlabelled examples a human should label next, usually those it is most uncertain about. It aims to reach good accuracy with far fewer labels, which saves annotation cost.
Active learning: an example
Out of 100,000 unlabelled support tickets, the model picks the 500 it is least sure about for agents to categorise first.
Related terms
More ML fundamentals terms
- Akaike information criterion
- Annotation
- Baseline model
- Bayesian information criterion
- Bias error
- Bias-variance tradeoff
- Classification
- Concept drift
Where you learn it
Active learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.