MATLAB Implementation of Random Forest Algorithm with Source Code

This resource provides comprehensive MATLAB source code implementations for Random Forest, a powerful ensemble learning method widely used for both classification and regression tasks in machine learning. The algorithm operates by constructing multiple decision trees during training and outputting the mode of classes (for classification) or mean prediction (for regression) from individual trees. Key implementation aspects include: the use of bootstrap aggregating (bagging) to create diverse tree subsets, random feature selection at each split to ensure decorrelation between trees, and OOB (Out-of-Bag) error estimation for validation. The code demonstrates proper handling of high-dimensional datasets while mitigating overfitting through built-in regularization mechanisms. Each MATLAB script includes detailed comments explaining critical functions such as tree construction parameters, split criterion calculations (Gini impurity or information gain), and prediction aggregation methods. The implementation showcases practical techniques for optimizing tree depth, managing feature importance scores, and parallelizing tree generation for improved computational efficiency. By studying these examples, developers will gain insights into effective parameter tuning strategies and learn to adapt the algorithm for various real-world machine learning applications.