Random Forest Implementation by the Original Algorithm Authors

This random forest implementation represents the original code developed by the algorithm's authors, featuring MATLAB-Fortran hybrid programming that requires Fortran compiler installation. Random forest is a supervised learning algorithm based on ensemble methods using decision trees. The implementation handles both classification and regression tasks with typically strong performance on diverse datasets. A key advantage of this algorithm is its capability to process high-dimensional data while maintaining robust performance with missing values. The core methodology combines multiple independent decision trees, where each tree trains on different data subsets using bootstrap aggregation (bagging). During the training phase, each node randomly selects feature subsets for splitting to reduce inter-tree correlation. For prediction, the algorithm aggregates results through majority voting (classification) or averaging (regression) across all trees. The code implementation specifically includes Fortran-compiled components for optimized computational efficiency in tree construction and splitting operations. This makes random forest an efficient and accurate machine learning method particularly suitable for large-scale data analysis.