SemiSupervised Learning
Semi-supervised learning is a machine learning technique that combines both labeled and unlabeled data for training models. In this approach, the algorithm learns from a small amount of labeled data and a large amount of unlabeled data to make predictions or classifications. This method is especially useful when labeled data is scarce or expensive to obtain, as it can leverage the abundance of unlabeled data to improve model performance.