Active learning, a sophisticated machine learning technique, empowers algorithms to intelligently and selectively determine which data samples should be sought for labeling, thereby optimizing the overall learning process in an efficient manner. This strategic approach proves to be especially invaluable in scenarios where the availability of labeled data is scarce, or when the tasks of data labeling are costly in terms of both financial resources and time consumption. By zeroing in on the data samples that carry the most significant information content, active learning not only significantly cuts down on the labeling expenses but also substantially enhances the performance and accuracy of the model.
