Question: In a concise analytical response, explain how the desired performance level and visual extrapolation of dev error are used to estimate the benefit of more training data.

Sample answer: First, add the desired performance level to the learning curve. Then visually extrapolate the dev-error curve to estimate how much closer increasing the training-set size could bring dev error to that target. The result should be treated as a plausibility judgment rather than a guarantee. In the passage's example, the curve suggested that doubling the training-set size might be sufficient to reach the desired performance.

Key points:

• Add the desired performance level to the learning curve.
• Visually extrapolate the dev-error curve.
• Estimate how much closer more data could bring performance to the target.
• Treat the estimate as plausible rather than guaranteed.
• Recognize that doubling the training-set size was the example conclusion.

Rubric: A strong response identifies the target line, explains visual extrapolation of the dev-error curve, connects the projection to the estimated benefit of more data, and characterizes the conclusion as plausible rather than certain.