Explore MATLAB source code curated for "决策树" with clean implementations, documentation, and examples.
MATLAB decision tree implementation - input your data and variable names to generate comprehensive decision tree results including model construction, visualization, and predictive analysis capabilities.
Three decision tree algorithm implementations (C4.5, ID3, CART_iris) collected from online sources for collaborative learning and research purposes.
MATLAB implementation of the ID3 decision tree algorithm for data mining applications, including code structure and key function explanations
Comprehensive collection of classification algorithms including SVM, kNN, and Decision Trees with detailed documentation and code implementation insights
MATLAB software provides a powerful and reliable environment for implementing ensemble decision tree algorithms, specifically random forest models. This implementation offers practical experience with random forest ensembles and demonstrates key decision tree concepts through customizable code parameters like tree depth and feature sampling.
ID3 serves as the cornerstone of decision tree classification methods, forming the basis for advanced techniques like C4.5 and CART. This implementation provides a MATLAB-based solution for ID3 classification, featuring core algorithm components such as entropy calculation, information gain computation, and recursive tree building.
High-quality MATLAB code for decision tree implementation - excellent for beginners with clear documentation and practical examples
Implementation of CART data mining decision tree algorithm using MATLAB with complete code structure and algorithm explanations
MATLAB source code for decision tree algorithm implementation, providing a reference for machine learning applications with code structure explanations
Implementation of CART algorithm including tree construction, pruning techniques, and visualization methods with code-level explanations