Question: Write a concise analytical response comparing decreasing model size with adding regularization when computational cost is not the concern.

Sample answer: Decreasing the number of neurons or layers can reduce variance, but it should be used cautiously and is not the recommended remedy. When computational cost is not the concern, adding regularization is generally preferable because it usually gives better classification performance. Regularization reduces variance, although it can increase bias.

Key points:

• Decreasing model size can reduce variance.
• The source recommends using model-size reduction with caution.
• Regularization usually gives better classification performance.
• Regularization is preferred when computational cost is not the concern.
• Regularization can reduce variance while increasing bias.

Rubric: A strong response explains that a smaller model can reduce variance, notes the source's caution, identifies regularization as the preferred option when computational cost is not a concern, and mentions the variance-bias tradeoff.