Case context: You are managing a machine learning project where training a model on 10,000 examples takes 10 hours. A junior engineer proposes generating a learning curve by training models on 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, and 10,000 examples.

Question: What should you diagnose about this proposed plan, and what alternative decision should you make based on the principles of learning curve efficiency?

Sample answer: I would diagnose that the junior engineer's plan is highly computationally expensive because it requires training many models on increasingly large, slow-to-train datasets (such as 7k, 8k, and 9k examples). Instead, I would decide to use nonlinearly spaced training-set sizes, such as 1,000, 2,000, 4,000, 6,000, and 10,000 examples. This decision significantly reduces total computation time while still giving a clear sense of the trends in the learning curve.

Key points:

• Diagnose the proposed plan as highly computationally expensive
• Propose an alternative using nonlinearly spaced sizes
• Justify that the alternative still shows clear trends
• Justify that it avoids the heavy cost of evenly spaced large datasets

Rubric: The response must recognize the high computational cost of the proposed linear plan and recommend a nonlinearly spaced alternative. It should justify the alternative by stating it avoids the cost of large intermediate sizes while still preserving clear learning curve trends.