Supervised learning involves training a model on a labeled dataset, where the input features and corresponding output labels are known. The goal is to learn a mapping from inputs to outputs that can be used to predict the outcomes of new, unseen data. Common algorithms include linear regression, decision trees, support vector machines, and neural networks.
