Case context: You have defined three initial error categories for a bird classification algorithm: 'Lighting issues', 'Small subject', and 'Blurry'. After categorizing 50 misclassified images, you notice that 15 of them involve birds partially hidden behind leaves or branches, which doesn't perfectly fit the initial categories.

Question: Based on Ng's recommendations for reviewing examples, what should your next immediate step be to improve your error analysis process, and what question should you ask yourself about these 15 images?

Sample answer: I should add a new category column to my spreadsheet, such as 'Occluded' or 'Hidden by foliage', to capture this specific pattern. While reviewing these 15 images, I should also ask myself how or whether a human could have correctly labeled the bird despite the obstruction, in order to inspire a potential solution.

Key points:

• Add a new error category column (e.g., occlusion) to the spreadsheet.
• Formulate the new category based on the observed pattern during manual review.
• Ask how or whether a human could correctly label the misclassified image.

Rubric: The response should explicitly state adding a new error category to the spreadsheet. It should also state asking if or how a human could label the images correctly.