Editing Active Learning (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Active learning''' — A machine learning approach where the model selects its own training examples to query for labels.
* '''Oracle''' — The entity providing labels in active learning; typically a human expert.
* '''Query strategy''' — The criterion used to select which unlabeled examples to request labels for.
* '''Uncertainty sampling''' — Query the example the model is least certain about (most uncertain prediction).
* '''Query by committee''' — Train multiple models (committee); query examples where they disagree most.
* '''Core-set selection''' — Select examples that best represent the overall data distribution geometrically.
* '''Expected model change''' — Query the example that would cause the largest change in the model if labeled.
* '''Expected error reduction''' — Query the example that would most reduce the model's expected error.
* '''Batch active learning''' — Selecting a batch of K examples at once (more practical than one-at-a-time).
* '''Pool-based sampling''' — Select from a pool of unlabeled examples (most common active learning setting).
* '''Stream-based sampling''' — Examples arrive sequentially; decide whether to query each one.
* '''Cold start''' — The initial problem in active learning: model is untrained, so uncertainty estimates are unreliable.
* '''Annotation budget''' — The total number of labels the user is willing to provide.
* '''Label efficiency''' — The ratio of performance improvement to annotation cost; active learning maximizes this.
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