Case context: A team is developing a machine learning system but is struggling with high bias and high variance. The team leader suggests they should avoid tuning hyperparameters and instead select a completely different model architecture that is well suited for the task.

Question: Based on the source, what should the team diagnose or decide regarding the team leader's suggestion to reduce both bias and variance simultaneously?

Sample answer: The team should decide to pursue the model architecture change because selecting an architecture well suited to the task is a valid method to reduce both bias and variance simultaneously. However, they must prepare for difficulty, as the source warns that selecting such an architecture is difficult and these methods are harder to identify and implement.

Key points:

• Selecting a model architecture well suited for the task can reduce bias and variance simultaneously.
• The team must acknowledge the difficulty of selecting such an architecture.
• Architectural changes are harder to identify and implement.

Rubric: The answer must state that selecting a task-suited model architecture can reduce both bias and variance simultaneously, but note the challenge that selecting such an architecture is difficult and hard to identify or implement.