CART Data Mining Decision Tree Algorithm Implementation in MATLAB

In this article, we explore how to implement the CART (Classification and Regression Trees) data mining decision tree algorithm using MATLAB. We begin by delving into the fundamental concepts of this algorithm, including the Gini impurity index for selecting optimal splitting features and the recursive binary partitioning process for tree generation. The implementation covers key MATLAB functions such as data import using readtable or csvread, data preprocessing with normalization techniques, and recursive tree construction using cell arrays or custom class structures.

Next, we demonstrate the MATLAB implementation approach, detailing how to structure the code for handling categorical and continuous variables, implementing the node splitting criteria using matrix operations for efficiency, and creating prediction functions that traverse the decision tree. The code implementation includes critical components like calculating impurity reduction, handling missing values, and implementing pruning strategies to prevent overfitting.

Finally, we examine practical applications of the CART algorithm in real-world scenarios such as customer segmentation, medical diagnosis, and financial risk assessment. The article provides insights into parameter tuning, visualization of decision trees using MATLAB's plotting capabilities, and performance evaluation using confusion matrices and cross-validation techniques. Through this comprehensive guide, you will gain deep understanding of CART algorithm mechanics and learn to apply MATLAB implementations to solve complex data classification and regression problems.