Active learning is a machine learning technique that involves iteratively selecting the most informative data points to label in order to improve the performance of a model while minimizing the labeling effort. In active learning, a model starts with a small labeled dataset and then actively chooses the instances it is uncertain about or expects to improve its performance the most. These instances are then labeled by an oracle (usually a human annotator) and added to the training set. This process continues iteratively, refining the model’s performance with each iteration.

Active learning is particularly useful when labeled data is scarce or expensive to obtain. By focusing on the most informative examples, the model can achieve higher accuracy with fewer labeled examples compared to traditional supervised learning approaches.

There are several common strategies used in active learning:

1. Uncertainty Sampling: This strategy selects instances for labeling where the model is most uncertain about its predictions. Examples include instances with high prediction entropy, low confidence, or low probability of the predicted class.
2. Query by Committee: This involves training multiple models on the same dataset and selecting instances where the models disagree the most. This helps in identifying instances that are ambiguous or difficult to classify.
3. Expected Model Change: This strategy selects instances that are expected to result in the largest change in the model’s predictions when labeled and added to the training set.
4. Density-Based Methods: These methods focus on selecting instances from regions where the data density is high, as these instances are more likely to be informative.
5. Multi-armed Bandits: This approach treats the active learning process as a sequential decision-making problem, where the goal is to balance between exploiting current knowledge and exploring uncertain regions.