How do I interpret a ROC curve?

What is a ROC curve and why is it important?

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, helping to evaluate the trade-offs between sensitivity and specificity.

How do I read the ROC curve?

To read a ROC curve, look for the area under the curve (AUC). AUC values range from 0 to 1, where 1 indicates perfect classification and 0.5 indicates no discriminative power. The closer the curve is to the top-left corner, the better the model's performance.

What does it mean if the AUC is 0.7?

An AUC of 0.7 suggests that the model has a fair ability to distinguish between the positive and negative classes. It's better than random guessing but indicates room for improvement.

Can you explain the significance of the threshold in a ROC curve?

The threshold determines the point at which you classify a prediction as positive or negative. Adjusting the threshold affects the true positive and false positive rates, which in turn shapes the ROC curve.