Case context: A product team has roughly 20 labeled defect photos from a new inspection line. It is debating whether to use a linear classifier or a small neural network, but it has barely considered how to represent the images through hand-crafted features.

Question: From the evidence given, what should the team treat as the more important issue, and what mistaken assumption should it avoid?

Sample answer: The team should treat feature design as the more important issue, because with only about 20 examples, careful feature construction can matter more than choosing between a linear classifier and a neural network. The team should avoid assuming that either model family will automatically be the better choice in such a small-data setting.

Key points:

• The dataset is extremely small.
• Feature engineering may have the biggest impact on performance.
• The specific model family may matter less than the features.
• Neither approach is guaranteed to win automatically.

Rubric: Full credit requires naming feature design as the priority, linking that priority to the roughly 20-example situation, and rejecting the idea that one model family is certain to outperform the other.