Google Professional Machine Learning Engineer
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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.
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.
Explanation:
Recall is the correct metric for this scenario because it measures the proportion of actual positives (real spam emails) that were correctly identified by the model. This is crucial for ensuring that as many spam emails as possible are caught, aligning with the primary goal of minimizing spam in users' inboxes. While precision (D) is important for reducing false positives (legitimate emails marked as spam), the immediate priority is to catch all spam emails, making recall the more relevant metric. Accuracy (A) and F-Score (B) provide broader measures of model performance but do not specifically address the need to identify all real spam emails.