C4.5 Decision Tree Matlab Implementation with Complete Algorithm Code

When implementing machine learning algorithms, the decision tree algorithm stands out as one of the most intuitive and easily understandable approaches. The C4.5 decision tree algorithm, in particular, has gained widespread practical adoption due to its effectiveness in handling both continuous and discrete attributes through information gain ratio calculations. This MATLAB implementation provides a complete workflow including data preprocessing, recursive tree construction using entropy-based splitting criteria, and pruning mechanisms to prevent overfitting. The program features key functions such as calculate_entropy() for measuring dataset impurity, find_best_split() that evaluates potential partitions using gain ratio, and build_tree() that recursively creates decision nodes until stopping conditions are met. We have conducted extensive testing with various datasets to ensure robust performance and proper handling of missing values. The code includes comprehensive comments explaining each algorithmic step, making it suitable for both educational purposes and practical applications. You can confidently integrate this implementation into your machine learning pipeline, as it follows the standard C4.5 specifications while providing MATLAB-specific optimizations for efficient computation. The program outputs the final decision tree structure with clear visualization capabilities and includes prediction functions for classifying new instances.