Implementation of CART Algorithm in Data Mining

The implementation of Classification and Regression Trees (CART) algorithm in data mining using MATLAB represents a fundamental machine learning technique. This algorithm recursively partitions datasets to construct decision tree models for prediction and classification tasks. During implementation, MATLAB's Data Mining Toolbox offers comprehensive functions and utilities for rapid CART model development and training. Key implementation steps include feature selection using MATLAB's statistical functions, attribute partitioning through recursive binary splitting algorithms, and pruning operations to prevent overfitting. The process involves critical functions like fitctree() for classification trees and fitrtree() for regression trees, which automatically handle Gini impurity calculations and mean squared error minimization. Through systematic optimization of node splitting criteria and post-pruning techniques using cross-validation, practitioners can significantly enhance model performance and prediction accuracy. Mastering CART algorithm implementation therefore becomes essential for effective data mining and machine learning applications.