Active learning
Active learning is a machine-learning technique in which a learner selectively chooses data examples for labeling or feedback to improve a model.
Machine Learning Algorithms in Depth
Active learning is a supervised learning technique in which a learner selects unlabeled instances for labeling to improve performance with fewer labeled examples.
- How it is used
- Selects unlabeled training instances for oracle labeling when labels are costly and fewer labeled examples are desired.
- How deep it goes
- Develops pool-based selection, compares query criteria, and implements a ranking class; flags full-pool ranking as costly at scale.
- Examples
- Compares 30 random labels with 30 entropy-selected queries on synthetic binary data, yielding 90% and 92.5% held-out accuracy.
- What you can do after
- Build it from scratch as a candidate-ranking class and apply it to logistic regression queries.
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AI-Powered Search
Active learning is a machine learning strategy in which the learner selects informative examples for labeling or feedback.
- How it is used
- Improves learning-to-rank systems by expanding feedback beyond results already favored by the deployed model.
- How deep it goes
- Explains presentation bias, exploration versus exploitation, Gaussian-process guidance, retraining, and A/B evaluation.
- Examples
- Uses unexplored search results, the “dryer” query, and an updated “transformers dvd” ranking experiment.
- What you can do after
- Apply it via an automated LTR pipeline that explores candidates, gathers signals, rebuilds judgments, retrains models, and reruns tests.
Machine Learning System Design
Active learning is a machine-learning technique that selectively chooses informative or uncertain examples for labeling or inclusion in a dataset.
- How it is used
- Guides dataset extension and labeling toward informative, uncertain, or error-prone examples rather than simply increasing volume.
- How deep it goes
- Defines error- and confidence-based selection, but defers detailed comparison of the many active-learning methods.
- Examples
- Uses PhotoStock query–image relevance pairs, model confidence, and model errors as concrete selection signals.
- What you can do after
- Apply active learning to prioritize human labeling and extend datasets with more informative examples.
Read how Machine Learning System Design explains active learning →
Related concepts
- training datasetaddresses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
- acquisition functionuses · An acquisition function is a scoring rule that ranks candidate evaluations by their expected usefulness for selecting the next sample or experiment.
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