Question: In a concise analytical response, explain why choosing between logistic regression and a neural network may be less important than feature hand-engineering with about 20 training examples.

Sample answer: With about 20 training examples, the way features are hand-engineered can affect performance more than choosing logistic regression or a neural network. A traditional algorithm is therefore not guaranteed to outperform a neural network, nor is the neural network guaranteed to win. The small-dataset advantage of neural networks is less consistent than their advantage in the huge-data regime, so feature design should receive substantial attention.

Key points:

• Feature hand-engineering can dominate algorithm choice with about 20 examples.
• Logistic regression and a neural network may perform similarly enough that the algorithm choice matters less.
• Traditional algorithms may or may not do better.
• Neural-network performance advantages are less consistent on small datasets than on huge datasets.

Rubric: A strong response identifies the small data regime, explains the greater potential influence of feature hand-engineering, rejects a guaranteed winner between the two algorithms, and notes that neural-network strength on small datasets is less consistent than on huge datasets.