Under which error metric can Total Error = Bias + Variance be expressed and proven with formulas?

Formal bias and variance formulas are always necessary to decide how to progress on an ML problem.

For mean squared error, Total Error = _____ + Variance.

Match each bias–variance idea with its role in the source.

Order the reasoning for choosing the appropriate bias–variance treatment.

Explain why formal and informal bias–variance treatments can serve different purposes.

Decide whether a team needs formal formulas or informal bias–variance definitions.

What does the source say is sufficient for practical bias–variance decisions?

Which approach best fits a practitioner whose only goal is choosing how to advance an ML project?

With mean squared error, formulas can specify bias and variance and prove their stated total-error relationship.