Collected Decision Tree Source Code from Online Resources

We present three decision tree source codes collected from online resources: C4.5, ID3, and CART_iris implementations. These codebases serve as valuable educational materials for understanding the implementation specifics of decision tree algorithms and their practical applications. Decision trees represent powerful machine learning algorithms capable of classifying complex datasets and making predictions. The C4.5 algorithm implementation typically features entropy-based attribute selection and handles continuous attributes through thresholding. The ID3 code demonstrates information gain calculations for discrete attributes using entropy reduction principles. The CART_iris implementation showcases classification and regression tree methodology applied to the classic iris dataset, including gini impurity calculations and binary splitting mechanisms. By studying these implementations, developers can enhance their programming skills and data analysis capabilities, establishing a solid foundation for future career development in machine learning. We hope these resources prove beneficial for learning and research endeavors.