← AI Terminology
Active Learning
Active learning lets a model select the most informative unlabeled examples for human annotation to maximise accuracy per labeling budget.
A classical strategy for expensive labels.
A classical strategy for expensive labels.
Why It Matters in AI
Labeling is often the bottleneck. Uncertainty sampling and diversity criteria focus human effort where it reduces error most — still highly relevant for enterprise classifiers and eval set construction.
Key Points
| Aspect | Description |
|---|---|
| Loop | Train → query informative x → label → retrain |
| Risk | Bias if query strategy skewed |
| Benefit | Fewer labels for same performance |
| LLM era | Also for preference data and eval mining |
| Related | Weak supervision, human-in-the-loop |
| Criteria | Uncertainty, margin, committee disagreement, coresets |
Simple Analogy
A teacher who asks students to bring the homework problems that confused them most — mark those first for maximum learning per minute.
Common Usage Examples
- Uncertainty sample low-confidence tickets
- Label only cluster exemplars
- Build golden sets via active mining
- Stop when gains plateau
Summary
In short: Active learning chooses the best examples to label next — maximising model quality per unit of human annotation.