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This article introduces decision tree algorithms, explains their role in supervised learning for classification and regression, details the construction process, compares information gain and Gini index for attribute selection, and reviews popular tree methods such as ID3, C4.5, and CART with illustrative examples.
Decision trees are tree-structured classifiers that split data using attributes chosen for maximal purity measured by Gini impurity or entropy, with algorithms like ID3 selecting splits by information gain, while overfitting is mitigated through constraints and pruning techniques such as REP, PEP, and CCP.
This article introduces decision trees in data analysis and machine learning, explains core concepts, compares popular algorithms such as ID3, C4.5, and CART, details their mathematical foundations, and provides a complete Python implementation with code examples for building and visualizing trees.