MATLAB Code for ROC Curve and Score Distribution Plotting

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Resource Overview

MATLAB-implemented code for generating ROC curves and score distributions with built-in performance metrics calculation, suitable for pattern recognition researchers and machine learning practitioners.

Detailed Documentation

This MATLAB-based implementation provides comprehensive tools for plotting Receiver Operating Characteristic (ROC) curves and score distribution visualizations. The core functionality includes automatic calculation of true positive rates (TPR) and false positive rates (FPR) using MATLAB's built-in statistical functions. The code implements threshold sweeping algorithms to generate smooth ROC curves and includes histogram-based score distribution plotting with customizable binning parameters. Researchers can extend the base functionality to incorporate feature extraction modules using MATLAB's Signal Processing Toolbox or implement additional classification algorithms through the Statistics and Machine Learning Toolbox. The modular architecture allows easy integration of custom performance metrics and supports batch processing for multiple datasets, making it suitable for comprehensive pattern recognition research workflows.

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