Case context: You are leading a machine learning effort in which training a model on 12,000 examples takes 9 hours. A new team member suggests building a learning curve by fitting models on 1,500, 3,000, 4,500, 6,000, 7,500, 9,000, 10,500, and 12,000 examples.

Question: What problem do you see with this plan, and what different set of training-set sizes would better fit a cost-conscious learning-curve study?

Sample answer: I would point out that this approach wastes a lot of compute, because it keeps retraining on many larger datasets that are each slow to process. A better choice is to use a smaller number of unevenly spaced training-set sizes, such as 1,500, 3,000, 6,000, and 12,000 examples. That keeps the experiment much cheaper while still revealing the overall shape of performance as the data size grows.

Key points:

• Identify the plan as unnecessarily expensive to run
• Recommend unevenly spaced training-set sizes instead of many evenly spaced ones
• Explain that the smaller set of runs still shows the main curve behavior
• Explain that the revised plan avoids repeated work on several costly large-data training jobs

Rubric: The response must notice that the proposed evenly spaced schedule is too costly and suggest a nonlinearly spaced alternative. It should explain that the alternative reduces the number of expensive training runs while still preserving the important trend information from the learning curve.