Active Learning

Active learning allows a model to identify which unlabelled examples would be most valuable for a human to annotate. It typically selects cases where the prediction is uncertain or where the example represents an underexplored region.

By prioritising informative samples, active learning can achieve strong performance with fewer labels. It is useful in medicine, scientific research and specialised business domains where expert annotation is costly.