MATLAB Implementation of Decision Tree Algorithm

Decision tree represents a fundamental and highly classical concept in machine learning. As a prominent algorithm for data analysis and prediction, decision trees are extensively utilized in data mining and decision support systems. The core principle involves recursively partitioning datasets into distinct subsets, where each subset corresponds to a specific decision or outcome. Through systematic data splitting and conditional judgments, decision trees facilitate the interpretation of underlying patterns and relationships within complex datasets. This algorithm serves as a crucial tool in machine learning with broad applications spanning medical diagnosis, financial risk assessment, marketing analytics, and environmental monitoring.

In MATLAB implementation, key functions include fitctree for classification trees and fitrtree for regression trees. The algorithm typically employs measures like Gini impurity or information gain for node splitting, while pruning techniques prevent overfitting. Code implementation involves data preprocessing, tree construction with recursive partitioning, visualization using view(tree) function, and prediction via predict method. The MATLAB environment provides integrated tools for cross-validation and hyperparameter optimization through the Classification Learner app or programming interface.

Thus, decision trees stand as not merely classical algorithms but powerful analytical instruments that enhance decision-making and predictive capabilities across diverse domains through interpretable model structures and efficient MATLAB computational frameworks.