Question: Write a brief analytical response explaining why the dev-error curve by itself is not enough when judging whether additional data will help, and how the training-error curve contributes.

Sample answer: The dev-error curve alone can be hard to extend into the future, so it may not tell you reliably what will happen if the training set becomes larger. Adding the training-error curve gives a second perspective that helps estimate how performance might change as more data is collected. For that reason, training error is best used alongside dev error when evaluating the benefit of more data.

Key points:

• The dev-error curve by itself is difficult to extend forward.
• The question is what may happen to dev error after adding more data.
• Training error provides a second curve.
• The second curve helps estimate the effect of more data.

Rubric: A strong response explains the difficulty of extending the dev-error curve, connects the issue to predicting dev performance with more data, and states that the training-error curve is an additional signal for estimating the effect of extra data.