MATLAB Code Implementation for Random Forest Classification

Random Forest is a powerful ensemble learning method that enhances classification accuracy and stability by constructing multiple decision trees and combining their prediction results. Implementing random forest classification in MATLAB typically involves multiple function files covering key modules such as data preprocessing, model training, prediction, and evaluation.

Data Preparation and Preprocessing The initial step involves loading and preprocessing the dataset, which usually includes splitting data into training and test sets. MATLAB provides various data partitioning functions like cvpartition for convenient random splitting. Data may require standardization or normalization to ensure stable model training.

Decision Tree Construction The core of random forest lies in building multiple decision trees. Each tree is trained using different data subsets and feature subsets to increase model diversity. MATLAB's TreeBagger function serves as the primary tool for implementing random forests, allowing specification of parameters such as the number of trees, maximum tree depth, and feature selection methods.

Model Training When training the model with the TreeBagger function, set the Method parameter to 'classification' for classification tasks. The NumPredictorsToSample parameter can be adjusted to control the number of features used per tree, typically set to the square root of the total feature count.

Prediction and Evaluation After training, use the predict function to make predictions on test data. MATLAB returns class labels and prediction probabilities for each sample. To evaluate model performance, calculate metrics like confusion matrix, accuracy, and recall rate using helper functions such as confusionmat and perfcurve.

Model Optimization Random forest performance depends on parameters like the number of trees and maximum depth. These can be optimized through cross-validation or grid search to improve the model's generalization capability.

By effectively combining these modules, random forest classification can be efficiently implemented in MATLAB for various machine learning applications.