If you want to reduce variance and extra computation is acceptable, which adjustment is usually preferred over shrinking the model?

Reducing a model’s size is usually the first choice for lowering variance when other options are available.

When trying to reduce variance, adding _____ is usually a better first step than simply shrinking the model.

Match each variance-control idea with its best description.

Arrange the steps for handling a model that appears to have high variance.

Why might a practitioner reduce model size even when another approach would better address variance?

Model Capacity and Variance

A smaller model is most useful when _____ training is the main goal, rather than only trying to lower variance.

Match each goal or situation to the technique or consequence most consistent with the guidance on model size and regularization.

Arrange the reasoning used to decide whether shrinking a model is a good response to high variance.

When Should You Shrink a Model Instead of Regularizing It?

Choosing a Variance Reduction Strategy for a Speech Model with Slow Training

When a Smaller Model Is a Reasonable Variance Fix