C4.5-Based Decision Tree Algorithm

The C4.5 algorithm is a classical method within the decision tree family, developed by Ross Quinlan as an improvement over the ID3 algorithm. Compared to ID3, C4.5 enhances model generalization by incorporating gain ratio for feature selection and implementing pruning strategies, enabling better handling of continuous data and missing values.

The core concept of C4.5 involves recursively selecting optimal features for data partitioning. Unlike ID3, which relies solely on information gain, C4.5 employs gain ratio as its feature selection criterion, preventing bias toward features with more values. For continuous features, the algorithm discretizes them using binary splitting to identify optimal cut points. Additionally, C4.5 controls tree complexity through pre-pruning and post-pruning techniques to avoid overfitting.

Due to its strong interpretability and no requirement for data normalization, the C4.5 algorithm finds wide applications in fields like medical diagnosis and credit assessment. However, when dealing with high-dimensional features, it’s essential to integrate feature selection techniques to optimize model performance.