Question: Under what condition is it worth choosing training-set sizes that are spaced unevenly instead of evenly?

Sample answer: This is useful when training every additional model for a standard evenly spaced learning curve would take a lot of compute time or money. Using fewer, strategically chosen sizes can reduce the cost of the experiment.

Key points:

• The method is chosen when training cost is high.
• It helps avoid spending too much compute on many extra models.

Rubric: The answer must state that uneven spacing is relevant when the cost of training the extra models is significant.