Question: Explain the ROC curve and its significance in binary classification.

Answer: The Receiver Operating Characteristic (ROC) curve is a graphical representation of a classifier's performance across various threshold settings. It plots the true positive rate against the false positive rate, helping to assess a model's trade-off between sensitivity and specificity.

Example:

A model with a higher Area Under the ROC Curve (AUC-ROC) is generally considered better at distinguishing between classes.