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

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

An 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 high TPR and a low FPR.

How can I use the ROC curve to compare different models?

You can compare models by looking at the area under the ROC curve (AUC). A higher AUC indicates a better model performance.

What is a good AUC value?

An AUC value of 0.5 indicates no discrimination (random guessing), while a value of 1.0 indicates perfect discrimination. Generally, an AUC above 0.7 is considered acceptable.