semi-supervised-learning

A learning approach that combines labeled and unlabeled data for training models.

Semi-supervised learning leverages a small amount of labeled data alongside a large amount of unlabeled data to improve model performance. This method is particularly useful when acquiring labeled data is expensive or time-consuming. By using unlabeled data, semi-supervised learning can enhance the generalization of models while reducing the need for extensive labeled datasets.