Scenario: A wildlife lab checks an image model that is supposed to flag photos containing foxes. In the validation set, 150 images really do show a fox. The model marks 90 of those as foxes and fails to identify the other 60.

Prompt: What recall does this model achieve for fox images, and what does that number mean in practical terms?

Sample answer: Recall is the number of true fox images that are correctly identified divided by the total number of true fox images. Here, that is 90 out of 150, which equals 0.60, or 60%.

This means the model detects 60% of the fox photos and misses 40% of the ones that actually contain a fox. If the team wants to find as many fox images as possible, this recall is not very strong.

Key points:

• Recall = correctly identified fox images / all actual fox images
• 90/150 = 60% recall
• 40% of the real fox images were missed
• Low recall means many true positives are not being caught