What are the applications of ROC AUC in machine learning?

What are the applications of ROC AUC in machine learning?

ROC AUC is widely used for evaluating the performance of classification models, especially in binary classification.

Can you explain how ROC AUC is calculated?

Sure! ROC AUC is calculated by plotting the true positive rate against the false positive rate at various threshold settings.

Why is ROC AUC preferred over accuracy?

ROC AUC is preferred because it provides a more comprehensive measure of model performance, especially with imbalanced datasets.

What are some real-world examples of using ROC AUC?

Real-world examples include medical diagnosis, fraud detection, and credit scoring, where model performance is critical.