Case context: A research team is building a system to predict monthly equipment failures in a factory. Their current model shows both high bias and high variance on evaluation data. One engineer proposes replacing the current architecture with a different design that may fit the task better, rather than spending time only on parameter tuning.

Question: According to the underlying principle, how should the team assess this proposal if the goal is to reduce both bias and variance at the same time?

Sample answer: The team should treat the proposal as reasonable in principle, because choosing an architecture that matches the task well can reduce both bias and variance together. At the same time, they should expect the search to be difficult, since finding and implementing such an architecture is often harder than making routine tuning changes.

Key points:

• A better-suited architecture can lower both bias and variance.
• The decision is not trivial because suitable architectures can be hard to identify.
• Implementing the change is often more difficult than standard tuning.

Rubric: The answer must say that a task-appropriate architecture can reduce both bias and variance simultaneously, and it must also mention that selecting and implementing such an architecture is difficult.