What is the importance of AUC-ROC in model evaluation?

What is the importance of AUC-ROC in model evaluation?

AUC-ROC is crucial as it helps evaluate the performance of a classification model at various threshold settings.

How does AUC-ROC differ from accuracy?

While accuracy measures the overall correctness, AUC-ROC considers true positive and false positive rates, providing a more nuanced view.

Can you explain what ROC curve represents?

The ROC curve plots the true positive rate against the false positive rate, illustrating the trade-off between sensitivity and specificity.

What does a higher AUC value indicate?

A higher AUC value indicates better model performance, meaning it has a higher probability of correctly distinguishing between classes.

Are there any limitations to using AUC-ROC?

Yes, AUC-ROC may not be suitable for imbalanced datasets, as it can give an overly optimistic view of model performance.