Active Learning

Active Learning is a machine learning paradigm where the algorithm selectively queries the most informative data points to improve its performance. This approach is particularly useful when labeled data is scarce or expensive to obtain, allowing the model to learn efficiently by focusing on uncertain or ambiguous cases. The main characteristics of Active Learning include the ability to prioritize data selection based on the model's current understanding and the iterative process of refining the model with newly labeled data. Common use cases include image classification, natural language processing tasks, and any scenario where labeling data is costly or time-consuming.