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Answer: Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute, Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
Precision and recall are critical metrics for evaluating models designed to identify specific content, such as offensive comments. Precision assesses the model's accuracy by determining the ratio of correctly identified positives to all predicted positives. Recall evaluates the model's ability to identify all actual positives. Together, they provide a comprehensive view of the model's performance in flagging inappropriate content. Option A focuses on the model's output, while option D provides a broader view by including a random sample of all messages, offering insights into the model's performance across the entire dataset.
Author: LeetQuiz Editorial Team
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In your role at a major social network, you're tasked with improving the efficiency of human moderators by developing a Machine Learning model to flag potentially offensive comments for review. The platform processes millions of comments daily, and the model must balance between identifying as many offensive comments as possible (high recall) and minimizing the number of non-offensive comments incorrectly flagged (high precision). Given the constraints of processing power and the need for timely moderation, which metric(s) would best evaluate the model's effectiveness? Choose the two most appropriate options.
A
Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute
B
Number of messages flagged by the model per minute, regardless of their nature
C
Number of messages flagged by the model as inappropriate per minute that are confirmed by humans
D
Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
E
Both A and D