Case context: A learning algorithm has high variance. The team is considering removing layers to make the model smaller, but computational cost is not a concern.

Question: What should the team consider doing instead, and why?

Sample answer: The team should consider adding regularization instead of decreasing the model size. Although a smaller model can reduce variance, the source advises caution and states that regularization usually gives better classification performance when computational cost is not the concern. The team should also recognize that regularization may increase bias.

Key points:

• Recommend adding regularization.
• Decreasing model size should be used cautiously.
• Computational cost is not the concern.
• Regularization usually gives better classification performance.
• Regularization reduces variance but can increase bias.

Rubric: Full credit requires recommending regularization, explaining its classification-performance advantage over reducing model size in this situation, and acknowledging its possible increase in bias.