Question: In a short explanation, relate a model's typical performance on the data it was fit on to its performance on held-out data, and use that to describe the positions of the two error curves.

Sample answer: A model is usually optimized to perform best on the training data. That usually makes the training error lower than the validation error. Since lower error is drawn lower on the graph, the validation-error curve usually sits above the training-error curve.

Key points:

• The model usually performs better on the training data.
• Better performance means lower error.
• Lower error places the training curve below the validation curve.

Rubric: A strong response should mention the typical performance gap, connect that gap to lower training error, and explain why the validation curve appears higher.