What is AUC-ROC and how is it used in practice?

What is AUC-ROC and how is it used in practice?

AUC-ROC stands for Area Under the Receiver Operating Characteristic curve. It's a performance measurement for classification problems at various threshold settings.

Can you explain how to calculate the AUC-ROC?

Sure! To calculate AUC-ROC, you first plot the ROC curve, which is a graph of the true positive rate against the false positive rate at different thresholds. The area under this curve represents the model's ability to distinguish between classes.

What does a higher AUC value indicate?

A higher AUC value, closer to 1, indicates a better model performance, meaning it has a higher true positive rate and a lower false positive rate.

Are there any limitations to using AUC-ROC?

Yes, AUC-ROC can be misleading in imbalanced datasets, as it may not reflect the model's performance on the minority class.