Active Learning

Active learning is a special type of semi-supervised learning where the algorithm itself determines which training data the user must label to achieve the best training results.

It is used when there is a need to maximize the efficiency of the labeling process, especially when labeling data is expensive or time-consuming. Active learning is commonly applied in scenarios such as image classification, natural language processing, and medical diagnosis. The technique works by selecting the most informative data points for labeling, which are expected to provide the most significant improvement to the model’s performance.

For example, in image classification, an active learning algorithm can identify the images that are most uncertain and request labels for those images from a human annotator. In natural language processing, the algorithm can select the sentences that are most ambiguous and ask for their correct labels.

Active learning is important because it allows for more efficient use of labeling resources, reducing the amount of labeled data needed to train a high-performing model. It is a powerful approach in machine learning, enabling models to learn more effectively from limited labeled data by focusing on the most informative examples.