What does the ROC curve represent in classification problems?

What does the ROC curve represent in classification problems?

Step 1: Understand that a classification problem involves predicting categories, like 'yes' or 'no'.
Step 2: Know that we can adjust a 'threshold' to decide how we classify something as 'yes' or 'no'.
Step 3: The true positive rate (sensitivity) is the percentage of actual 'yes' cases that we correctly identify as 'yes'.
Step 4: The false positive rate is the percentage of actual 'no' cases that we incorrectly identify as 'yes'.
Step 5: The ROC curve is a graph that shows how the true positive rate and false positive rate change as we adjust the threshold.
Step 6: A good model will have a high true positive rate and a low false positive rate, which means it will be closer to the top left corner of the ROC curve.

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