What is active learning and when should you use it?
mediumAnswer
- Iteratively query most informative unlabeled examples for human labeling.
- Strategies: (1) Uncertainty sampling (max entropy / min margin / least confidence).
- (2) Query-by-committee (disagreement between ensemble).
- (3) Diversity sampling (core-set) to avoid redundant queries.
- (4) BALD (Bayesian mutual information).
- Use when labels are expensive (medical, legal, expert domains).
- Skip when labels are cheap (crowdsourcing) or class balance matters more than label count.
Check yourself — multiple choice
- Random
- Iteratively query most informative unlabeled: uncertainty / QBC disagreement / diversity core-set / BALD mutual info; use when labels expensive (medical/legal)
- Random labels
- Not real
Active learning: uncertainty / QBC / diversity / BALD; expensive labels.
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