Question: Explain why recall alone does not give a complete picture of how well a bird detector performs on a validation set.

Sample answer: Recall tells us what fraction of the actual bird images were correctly identified as birds. It does not say anything about how often non-bird images were incorrectly labeled as birds. A detector could score very high on recall by marking nearly everything as a bird, which would capture most true bird images but also create many false alarms. Because recall focuses only on the positive class, it cannot by itself show whether the detector is reliable or accurate overall.

Key points:

• Recall = percentage of actual bird images correctly labeled as bird
• Recall does not measure false positives on non-bird images
• A model can raise recall by predicting the bird label too often
• Recall alone is not enough to judge overall performance

Rubric: Full credit: correctly defines recall, explains that it ignores false positives/non-bird images, and explains how over-predicting birds can inflate recall. Partial credit: gives the definition but does not clearly explain the limitation. No credit: gives an incorrect definition or no explanation of why recall is incomplete.