What characterizes semi-supervised learning?

Semi-supervised learning is characterized by the combination of labeled and unlabeled data during the training process. In this approach, a smaller set of labeled data is used alongside a much larger set of unlabeled data. The labeled data provides necessary guidance for the model to learn specific patterns, while the unlabeled data helps enhance the model's understanding of the underlying structure in the data, thereby improving generalization and decision-making.

This method is particularly useful in scenarios where acquiring labeled data is expensive or time-consuming, whereas unlabeled data may be relatively easy to collect. By leveraging both types of data, semi-supervised learning strikes a balance that allows models to achieve better performance than they would if trained solely on a limited amount of labeled data. It effectively utilizes the abundant unlabeled data to improve predictive accuracy while minimizing the reliance on costly labeled datasets.