Case context: A team evaluates their cat classifier on the dev set. Out of 200 true cat images in the dev set, the classifier correctly labels only 120 of them as cats, missing the remaining 80.

Question: Based on the definition of Recall, what would this classifier's Recall be, and what does this tell the team about its performance on true cat images?

Sample answer: The classifier's Recall would be 120 out of 200, or 60%. This means that out of all the actual cat images in the dev set, the classifier correctly identified only 60% of them as cats, missing 40% of true cat images. This indicates the classifier is failing to catch a substantial portion of actual cats, which could be a problem if the team's goal is to identify as many cat images as possible from the set.

Key points:

• Recall = correctly labeled cat images / total actual cat images
• 120/200 = 60% Recall
• 40% of true cat images were missed
• Low Recall indicates the classifier fails to catch many actual cats

Rubric: Full credit: correctly calculates Recall as 120/200 = 60% and explains that this reflects missing 40% of true cat images. Partial credit: correct calculation without explanation or vice versa. No credit: incorrect calculation or explanation.