Active Learning is a training approach in which a model or learning system selectively requests the examples or labels that are most useful for improving performance. It is especially valuable when labeled data is scarce, expensive, or dependent on expert human annotation.
A training approach in which the algorithm chooses some of the data it learns from. Active learning is particularly valuable when labeled examples are scarce or expensive to obtain. Instead of blindly seeking a diverse range of labeled examples, an active learning algorithm selectively seeks the particular range of examples it needs for learning.