Semi-supervised machine learning is a combination of supervised and unsupervised machine learning methods. With more common supervised machine learning methods, you train a machine learning algorithm on a “labeled” dataset in which each record includes the outcome information. This allows the algorithm to deduce patterns and identify relationships between your target variable and the rest of the dataset based on the information it already has. In contrast, unsupervised machine learning algorithms learn from a dataset without the outcome variable. In semi-supervised learning, an algorithm learns from a dataset that includes both labeled and unlabeled data, usually mostly unlabeled.