Random Forest in MATLAB

In machine learning, Random Forest is a classifier comprising multiple decision trees, where the output class is determined by the majority vote of individual tree predictions. Developed by Leo Breiman and Adele Cutler, this algorithm integrates "Bootstrap aggregating" and "random subspace method" for robust ensemble learning. This translation includes MATLAB-specific implementation insights for decision tree training, feature sampling, and aggregation techniques.