What is the difference between AUC-ROC and Precision-Recall?

What is the difference between AUC-ROC and Precision-Recall?

AUC-ROC measures the performance of a classification model at all classification thresholds, while Precision-Recall focuses on the performance of a model with respect to positive class predictions.

When should I use Precision-Recall instead of AUC-ROC?

Use Precision-Recall when dealing with imbalanced datasets, as it provides a clearer picture of the model's performance on the positive class.

Can you explain how to calculate AUC-ROC?

AUC-ROC is calculated by plotting the true positive rate against the false positive rate at various threshold settings and measuring the area under the curve.

What are the limitations of using AUC-ROC?

AUC-ROC can be misleading in imbalanced datasets, as it may give an overly optimistic view of model performance.