What is AUC-ROC and why is it important?

What is AUC-ROC and why is it important?

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

How do you interpret the AUC value?

The AUC value ranges from 0 to 1. A value of 0.5 indicates no discrimination, while a value of 1 indicates perfect discrimination.

Can you explain what the ROC curve represents?

The ROC curve plots the true positive rate against the false positive rate at different thresholds, showing the trade-off between sensitivity and specificity.

Why is AUC-ROC preferred over accuracy?

AUC-ROC is preferred because it considers all possible classification thresholds, providing a more comprehensive evaluation of model performance, especially with imbalanced datasets.