How do I evaluate my model using the ROC curve?

How do I evaluate my model using the ROC curve?

To evaluate your model using the ROC curve, you first need to calculate the true positive rate and false positive rate for different thresholds.

What does the area under the ROC curve (AUC) represent?

The AUC represents the likelihood that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

How can I plot the ROC curve in Python?

You can use libraries like Matplotlib and Scikit-learn. First, compute the ROC curve using roc_curve and then plot it using plt.plot().

Are there any limitations to using the ROC curve?

Yes, the ROC curve can be misleading if the classes are imbalanced, as it may give an overly optimistic view of the model's performance.