Active Learning

Active learning reduces labeling work by letting a model help choose which examples should be reviewed next. Instead of asking people to label a random sample, the system selects records it finds uncertain, informative, or representative of an overlooked region. Human answers are added to the training set, the model is updated, and the cycle repeats. This approach can be valuable when expert labels are expensive, such as in medicine, law, or industrial inspection. Poor selection strategies can focus too narrowly or amplify existing blind spots, so teams monitor coverage, reviewer consistency, class balance, and whether improved benchmark scores translate into better real-world performance.