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In the context of developing a spam email classification model for a large email service provider, you are tasked with selecting the most appropriate metric to evaluate the model's performance. The primary goal is to ensure that the model accurately identifies as many real spam emails as possible, minimizing the risk of spam emails reaching users' inboxes. Given the constraints of handling millions of emails daily and the need for real-time processing, which metric should you prioritize to accurately measure the percentage of real spam emails that were correctly recognized by the model? Choose the best option.
A
Accuracy, as it provides a general measure of the model's correctness across all classifications.
B
F-Score, since it balances the trade-off between precision and recall, offering a single metric for model evaluation.
C
Recall, because it directly measures the proportion of actual spam emails that were correctly identified by the model.
D
Precision, as it focuses on the proportion of emails classified as spam that are indeed spam, reducing false positives.