ROC Curve Plotting Function for Binary Classification Models

ROC curves serve as essential tools for assessing binary classification model performance. This function generates curves that visually represent model classification capability by calculating true positive rates (TPR) and false positive rates (FPR) across different probability thresholds. The implementation typically involves sorting prediction probabilities from the test set and iterating through threshold values ranging from 0 to 1.

The algorithm generally follows these steps: First, prediction probabilities are sorted in descending order. Then, the function iterates through threshold values, calculating the four fundamental confusion matrix metrics (TP, FP, TN, FN) at each threshold. TPR (plotted on the y-axis) represents the proportion of positive cases correctly identified, while FPR (x-axis) indicates the proportion of negative cases incorrectly classified as positive. The implementation may use cumulative sums to efficiently compute these rates as thresholds vary.

Ideally, the curve hugs the top-left corner, while the diagonal represents random guessing performance. The function often includes AUC (Area Under Curve) calculation as a quantitative metric, where values closer to 1 indicate stronger model discriminative ability. AUC computation typically employs numerical integration methods like the trapezoidal rule.

For multiclass problems, the function commonly employs a "one-vs-rest" strategy, generating separate ROC curves for each class. Proper implementation requires both true class labels and model-generated probability scores as inputs, with validation checks for input dimensions and value ranges to ensure calculation accuracy.