MATLAB Implementation of Decision Tree Algorithm with Code Examples

The MATLAB code for decision tree implementation is highly effective and particularly suitable for beginners. Decision tree is a widely-used machine learning algorithm that constructs tree-based decision models for classification and prediction tasks. This algorithm decomposes complex datasets into simple decision rules, making problem interpretation and understanding more intuitive and accessible. The MATLAB implementation provides convenient tools and functions that enable users to quickly build and train decision tree models, followed by prediction and classification on new data. Key features include the use of MATLAB's ClassificationTree and RegressionTree functions, which support various splitting criteria such as Gini impurity and information gain. The code includes practical examples of tree pruning techniques to prevent overfitting, visualization methods using the view() function to display tree structure, and comprehensive parameter tuning options for optimal performance. Whether for academic research or practical applications, decision tree remains a powerful and versatile algorithm. The MATLAB implementation specifically offers built-in functions like fitctree for classification and fitrtree for regression tasks, along with cross-validation methods and performance evaluation metrics to ensure robust model development. The code structure emphasizes readability and includes detailed comments to guide users through each step of the decision tree creation process.