Case context: A team has about 20 training examples. It is debating whether to use logistic regression or a neural network, but it has spent little time considering how the features are hand-engineered.

Question: Based only on the source evidence, what should the team diagnose as the potentially more important issue, and what assumption should it avoid?

Sample answer: The team should diagnose feature hand-engineering as the potentially more important issue because, with about 20 examples, it can have a bigger effect than choosing logistic regression or a neural network. The team should avoid assuming that either traditional algorithms or neural networks will automatically perform better in the small data regime.

Key points:

• The dataset is in a small data regime.
• Feature hand-engineering may have the bigger effect.
• The logistic-regression-versus-neural-network choice may matter less.
• Neither model family is guaranteed to perform better.

Rubric: Full credit requires identifying feature hand-engineering as the priority, connecting that priority to the approximately 20-example setting, and rejecting a guaranteed performance winner between traditional algorithms and neural networks.