In a machine learning project, you are comparing the performance of two models using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric. Model A has an AUC of 0.92, while Model B has an AUC of 0.87. However, you notice that Model A has a higher false positive rate (FPR) at a given true positive rate (TPR) compared to Model B. Which of the following statements is true, and why?

Simulated

Model A is better than Model B because it has a higher AUC.

9.6%

Model B is better than Model A because it has a lower FPR at the same TPR.

42.3%

Model A is better for imbalanced datasets, because it has a higher AUC.

17.3%

Model B is better for balanced datasets, because it has a lower FPR at the same TPR.

30.8%

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