Implementation of CART Algorithm in MATLAB
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Resource Overview
A MATLAB-based implementation of CART (Classification and Regression Trees) algorithm for pattern recognition tasks, supporting both classification and regression analysis. The package includes detailed documentation, sample routines, and code explanations covering key functions like tree building, node splitting using Gini impurity, and pruning techniques.
Detailed Documentation
This document provides comprehensive details about the CART algorithm implementation program. Developed in MATLAB, this implementation facilitates classification and regression analysis in pattern recognition applications. The core algorithm features recursive binary splitting with Gini index minimization for classification tasks and variance reduction for regression. The package includes the complete source code with annotated functions for tree construction, prediction methods, and cross-validation routines. Additionally, detailed documentation and practical examples demonstrate data preprocessing, model training with stopping criteria, and visualization of decision boundaries. Through this implementation, users can effectively process and analyze datasets, gaining deeper insights through interpretable tree-based models with configurable parameters for maximum depth and minimum leaf size.
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