C4.5 Decision Tree Algorithm Source Code

This article explores the source code implementation of the C4.5 decision tree algorithm. As one of the classic algorithms in the decision tree domain, C4.5 generates classification and prediction models through data learning and analysis. The algorithm's core implementation involves calculating information gain ratios for attribute selection, handling missing values through probability distribution, and converting continuous attributes to discrete intervals via dynamic thresholding. C4.5 has been widely applied across various fields including finance, healthcare, and natural language processing, with over 10,000 academic citations demonstrating its significant impact in both research and practical applications. Our discussion will examine the algorithmic principles—such as recursive tree building with stopping conditions and post-pruning optimization techniques—along with real-world use cases to facilitate deeper understanding and effective utilization of this foundational machine learning method.