Decision Trees (DTs) are a non-parametric supervised learning method used for both classification and regression tasks. They model decisions in a flowchart-like tree structure, mimicking how humans make choices through a series of questions or tests. In a decision tree, each internal node represents a question or test on a feature (e.g., “Is age > 30?”), each branch represents an outcome of that test (e.g., yes/no), and each leaf node represents a final decision or prediction (a class label or numerical value).