Case context: A learning curve chart shows training error in teal and validation error in orange. Across the full plot, the orange line remains higher than the teal line.

Question: Is this the expected pattern for training and validation error, and what does it say about how the model performs on the two data sets?

Sample answer: Yes. This is the usual pattern. When validation error is above training error, it means the model fits the training set more closely than the validation set. In other words, performance is better on the training data than on the held-out data.

Key points:

• Orange is the validation-error curve.
• Teal is the training-error curve.
• The validation curve is higher throughout the chart.
• The model performs better on the training set than on the validation set.

Rubric: The response should recognize the pattern as normal and correctly explain that a higher validation-error curve means the model is doing better on the training data.