Semi-Supervised Learning

Semi-supervised learning is a type of machine learning where the training data contains both labeled and unlabeled examples. The goal is to leverage the information from the unlabeled data to improve the performance of a model that would otherwise be trained solely on the labeled data. This approach is particularly useful when obtaining labels is expensive or time-consuming, but unlabeled data is readily available.