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Decision tree algorithms are a class of supervised learning methods used for classification and regression. They predict a target variable by learning a set of decision rules inferred from data. A tree consists of internal decision nodes, branches, and leaf nodes that contain predictions. Each internal node tests a feature value; traversal from the root to a leaf yields a prediction.
Construction: Most algorithms use greedy top-down induction. At each step, the algorithm selects the feature and
Variants: CART (Classification and Regression Trees) handles both classification and regression and uses the Gini impurity
Training and pruning: Pruning reduces overfitting by removing branches that do not provide power on unseen
Strengths and limitations: Decision trees are interpretable and fast at inference, handle mixed data types, and
Applications: broad use in medicine, finance, policy and marketing for classification and regression tasks, such as
History: Early work includes CART by Breiman et al. (1984), ID3 by Quinlan (1986), and C4.5 as