ROC Curve Plotting Source Code Implementation

This source code provides a comprehensive implementation for plotting ROC (Receiver Operating Characteristic) curves, specifically designed to assist beginners in machine learning evaluation. The code demonstrates how to calculate true positive rates (TPR) and false positive rates (FPR) at various classification thresholds, then plot the characteristic curve that visualizes model performance. Key functions include threshold sorting algorithms, cumulative probability calculations, and AUC (Area Under Curve) computation methods. The implementation features step-by-step comments explaining the mathematical logic behind each ROC curve component, including how to handle binary classification outputs and generate the corresponding coordinate points. Users can modify threshold parameters, adjust curve smoothing techniques, and integrate custom scoring functions. This practical implementation serves as an educational tool for understanding model evaluation metrics while providing a solid foundation for advanced ROC curve applications in data science projects. The code structure emphasizes clarity with modular functions for data preprocessing, metric calculation, and visualization components, enabling easy adaptation to different datasets and classification scenarios.