How do I interpret a ROC curve?

What is a ROC curve and how do I interpret it?

A ROC curve, or Receiver Operating Characteristic curve, is a graphical representation of a classifier's performance. It plots the true positive rate against the false positive rate at various threshold settings.

What do the axes of the ROC curve represent?

The x-axis represents the false positive rate (FPR), while the y-axis represents the true positive rate (TPR). A good model will have a curve that bows towards the top left corner.

How can I use the area under the ROC curve (AUC) to evaluate my model?

The AUC provides a single scalar value to assess the model's performance. An AUC of 1 indicates perfect classification, while an AUC of 0.5 suggests no discrimination ability.

What is a good AUC value for a model?

Generally, an AUC value above 0.7 is considered acceptable, above 0.8 is good, and above 0.9 is excellent. However, context matters!