Reducing Human Annotation with ML Active Learning

Active learning is a machine learning approach that reduces manual annotation costs by allowing a model to select the most informative unlabeled samples for a human to label. The process works in an iterative cycle where the model identifies data points it is most uncertain about and requests labels only for those, thereby improving performance with fewer labeled examples. This guide explains several common query strategies, including uncertainty sampling, diversity-based sampling, and query by committee. It also provides practical Python code examples for implementing these techniques, from training an initial model to scoring and selecting new data for annotation.