What is the Area Under the ROC Curve (AUC) used for?

The Area Under the ROC Curve (AUC) is primarily used to evaluate classification models, focusing on their ability to distinguish between different classes. The ROC curve itself is a graphical representation that illustrates the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The AUC quantifies the overall ability of the model to correctly classify positive and negative instances, regardless of the specific decision threshold chosen.

AUC values range from 0 to 1, with a value of 0.5 indicating no discriminative power (similar to random guessing) and a value of 1.0 indicating perfect separation of classes. Thus, a higher AUC value suggests a model that has better predictive performance and is more reliable in classifying data points into the appropriate categories.

In contrast, options discussing regression models, API service quality, and text-to-speech conversions do not align with the specific use case of AUC, which is exclusively related to classification tasks. Therefore, focusing on the model's classification abilities makes option B the correct choice.