The Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) are widely used in binary classification tasks to evaluate the performance of a classification model. These metrics provide insights into the trade-off between the true positive rate and the false positive rate at different classification thresholds.
I. Receiver Operating Characteristic (ROC) Curve:
The ROC curve is a graphical representation of a classification model’s performance across various threshold settings. It plots the True Positive Rate (TPR), also known as sensitivity or recall, against the False Positive Rate (FPR), which is equal to 1−Specificity.
1. True Positive Rate (Sensitivity/Recall):
TPR=True Positives / (True Positives + False Negatives)
2. False Positive Rate:
FPR=False Positives / (False Positives + True Negatives)
A point on the ROC curve represents the performance of the classifier at a specific threshold. The curve helps visualize the trade-off between sensitivity and specificity.
