What is the ROC curve and AUC in machine learning?

What is the ROC curve and AUC in machine learning?

The ROC curve is a graphical representation of a classifier's performance, plotting true positive rate against false positive rate.

How do you interpret the AUC value?

The AUC, or Area Under the Curve, quantifies the overall ability of the model to discriminate between positive and negative classes. A value of 1 indicates perfect classification, while 0.5 suggests no discrimination.

What are some common applications of ROC and AUC?

ROC and AUC are commonly used in binary classification problems, such as medical diagnosis, credit scoring, and spam detection.

Can you explain how to create a ROC curve?

To create a ROC curve, you need to calculate the true positive and false positive rates at various threshold settings, then plot these rates on a graph.