MATLAB Implementation for Computing ROC Curve and AUC

This MATLAB program provides a robust implementation for computing ROC curves and AUC values, essential metrics for evaluating binary classification systems. The ROC curve graphically represents classifier performance by plotting True Positive Rate (TPR) against False Positive Rate (FPR) across various classification thresholds. The implementation includes efficient sorting algorithms to rank prediction scores and calculate cumulative statistics for TPR/FPR coordinates. The program features threshold-sensitive coordinate generation where each point corresponds to a specific discrimination threshold, with automated calculation of the AUC using the trapezoidal rule integration method. Key functions include probability score sorting, cumulative sum operations for TP/FP counts, and normalized rate calculations. The AUC computation measures classifier discriminative power through numerical integration of the ROC curve, where values closer to 1.0 indicate superior classification performance. This user-friendly tool offers customizable parameters for threshold granularity and scoring metrics, accompanied by detailed inline comments explaining each computational step. The code structure allows easy modification of classification score processing and visualization options, making it particularly valuable for machine learning validation and model comparison tasks. Researchers can leverage this implementation for systematic classifier evaluation with professional-grade statistical output and configurable plotting capabilities.