You have a highly imbalanced dataset with rare positive cases. Which performance metric would be the most informative, and why?
- AUC, as it provides a comprehensive evaluation of the model
- Accuracy, as it gives overall performance
- F1-Score, as it balances Precision and Recall
- Precision, as it focuses on false positives
In a highly imbalanced dataset, F1-Score is often most informative as it balances Precision and Recall. Accuracy might be misleading, and while AUC and Precision are useful, F1-Score provides a better overall sense of how well the model handles both classes.
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