Active Learning

A training approach where the model identifies which data points it is most uncertain about and requests labels for those specific examples, rather than being trained on a randomly selected dataset. This makes active learning particularly useful when labeled data is expensive or time-consuming to produce, as human annotation effort is focused where it matters most, achieving strong performance with considerably less labeled data.