This category covers supervised learning methods for predicting classes or continuous values from labeled data. Techniques include neural networks, decision trees, support vector machines, and ensemble models, with a focus on building models that generalize well. Applications range from cancer subtype prediction and drug response estimation to cell type classification in single-cell RNA-seq data.