Active learning is a method for improving the efficiency of data labels. The hope here is that ML models can achieve greater accuracy with fewer training labels if they can choose which data samples to learn from. Active learning is sometimes called query learning—though this term is getting increasingly unpopular—because a model (active learner) sends back queries in the form of unlabeled samples to be labeled by annotators (usually humans).
