Random Forest in MATLAB

In the field of machine learning, Random Forest serves as a classifier that constructs multiple decision trees and determines the final output class through a majority vote of the individual tree predictions. The algorithm was developed and derived by Leo Breiman and Adele Cutler, who trademarked the term "Random Forests." The concept originated from Tin Kam Ho at Bell Labs in 1995, based on the idea of random decision forests. This method combines Breiman's "Bootstrap aggregating" (bagging) technique with Ho's "random subspace method" to build an ensemble of decision trees. In MATLAB implementation, key functions like `TreeBagger` (from the Statistics and Machine Learning Toolbox) are commonly used to train Random Forest models. The algorithm involves: 1. Bootstrapping: Creating multiple training subsets by sampling with replacement. 2. Feature randomness: Selecting a random subset of features at each node split to decorrelate trees. 3. Aggregation: Combining predictions using majority voting (classification) or averaging (regression). The introduction of Random Forest provides a powerful and efficient classifier for machine learning applications, adaptable to various domains and problem types through MATLAB's streamlined workflow for hyperparameter tuning and model validation.