Question: Why is it often difficult to choose a model-architecture change that lowers both bias and variance at the same time?

Sample answer: Because useful architecture changes are often hard to discover, hard to implement correctly, and hard to tune for the specific dataset and task. A design that works well for one problem may not transfer well to another.

Key points:

• Architecture changes can be difficult to identify.
• They can be difficult to implement and tune.
• The best architecture depends on the task and data.

Rubric: The student should explain that architecture changes are hard to find and implement, and that selecting an architecture well matched to the task is difficult.