Decision Tree Implementation in MATLAB

This is a MATLAB-based source program implementing decision tree algorithms, available for reference and study. Decision trees represent a fundamental machine learning approach applicable to both classification and regression problems. This implementation utilizes MATLAB's programming environment to construct decision tree models through recursive partitioning of input data. The algorithm operates by evaluating features at each node, selecting optimal split points based on criteria like information gain or Gini impurity. The source code demonstrates key components including: - Data preprocessing and feature selection mechanisms - Node splitting algorithms with stopping conditions - Tree pruning methods to prevent overfitting - Prediction functions for new data classification This implementation serves as an educational resource for understanding decision tree fundamentals, MATLAB programming techniques for machine learning, and practical applications for solving real-world pattern recognition problems. The code structure emphasizes modular design with separate functions for tree construction, traversal, and validation. The program provides insights into handling categorical and continuous variables, visualizing decision boundaries, and evaluating model performance using confusion matrices and cross-validation. We hope this resource facilitates deeper understanding of decision tree mechanics and their effective implementation in MATLAB environments.