CART Algorithm for Decision Tree Implementation

In this document, we present a MATLAB implementation of the CART (Classification and Regression Trees) algorithm for decision tree construction. The algorithm demonstrates excellent performance in generating reliable results for both classification and regression problems. Our implementation utilizes key MATLAB functions including tree node creation, recursive partitioning with Gini impurity calculations, and pruning optimization techniques. Through the CART algorithm, we can effectively perform data classification and prediction tasks with high accuracy.

The algorithmic implementation is particularly significant as it enables better decision-making and analytical capabilities through its binary recursive partitioning approach. Our MATLAB code has been meticulously debugged and optimized, incorporating features like cross-validation for parameter tuning and impurity-based splitting criteria, ensuring both computational efficiency and predictive accuracy. The implementation handles various complex datasets through automatic feature selection and optimal split point identification, consistently delivering satisfactory results.

CART algorithm serves as a powerful tool with wide applications across multiple domains, employing techniques such as cost-complexity pruning to prevent overfitting. We take pride in developing this robust implementation and successfully applying it to our projects, demonstrating its versatility in handling real-world machine learning challenges through structured tree-based modeling.