What does a high AUC (Area Under the Curve) value indicate in a ROC curve?

What does a high AUC (Area Under the Curve) value indicate in a ROC curve?

Step 1: Understand what a ROC curve is. A ROC curve is a graph that shows the performance of a classification model at different threshold settings.
Step 2: Know that the AUC stands for Area Under the Curve. It measures the entire two-dimensional area underneath the ROC curve.
Step 3: Realize that the AUC value ranges from 0 to 1. A value of 0.5 means the model has no discrimination ability (like random guessing).
Step 4: Recognize that a high AUC value (close to 1) indicates that the model can effectively distinguish between the positive and negative classes.
Step 5: Conclude that a high AUC value means the model is good at predicting the correct class for new data.

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