When should I use a ROC curve?

When should I use a ROC curve?

A ROC curve is useful when you want to evaluate the performance of a binary classification model, especially when dealing with imbalanced datasets.

What does ROC stand for?

ROC stands for Receiver Operating Characteristic. It illustrates the trade-off between sensitivity and specificity.

How do I interpret the area under the ROC curve?

The area under the ROC curve (AUC) represents the model's ability to distinguish between classes. AUC values range from 0 to 1, with 1 being perfect accuracy.

Can I use ROC curves for multi-class classification?

Yes, you can use ROC curves for multi-class classification by using techniques like one-vs-all or one-vs-one.

What are some limitations of ROC curves?

ROC curves can be misleading in highly imbalanced datasets and do not provide information on the actual predicted probabilities.