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After marking the target performance level on a development-set error curve, extending the trend by eye can provide a rough estimate of whether a larger training set might close the gap. In the example, the projected shape suggested that doubling the data could plausibly bring performance to the target.

Using a Development-Set Curve to Estimate the Payoff of More Data

What does sketching an extension of the dev-error curve help you estimate?

A trend line from past data can give an exact guarantee of the model’s future error after more training data is added.

Add the target _____ to a dev-error curve before estimating the value of more data.

Match each learning-curve element with its role when estimating the benefit of more labeled data.

Order the reasoning steps for judging whether more data is likely to reach a target performance level.

Question:
In a short analytical response, explain how a target error level and a plotted dev-loss trend can be used to judge whether collecting additional training examples is worthwhile.

Sample answer:
Mark the desired error level on the graph, then extend the dev-loss trend beyond the observed points to estimate how much extra data might be needed to approach that level. This gives a rough planning estimate, not a promise of what will happen. In the example, the trend suggested that a larger training set, roughly about twice as large, could be enough to get near the goal.

Key points:
- Mark the goal level on the plot.
- Extend the dev-loss trend beyond the current data.
- Use the projection to estimate the possible gain from more examples.
- Treat the result as an approximation, not a certainty.
- Note that the example pointed to roughly doubling the training set.

Rubric:
A strong response states how to place the target on the curve, describes extending the dev-loss trend to estimate the effect of added data, and explains that the conclusion is only an estimate rather than a guarantee.

Using a Validation Curve to Judge the Value of More Data

Case context:
A model team draws a target validation-error level on a learning curve. By extending the observed trend beyond the current sample count, the curve appears to meet that target at roughly two times the present data size.

Question:
What does this suggest about the value of collecting additional examples, and how definite should that judgment be?

Sample answer:
This suggests that increasing the data set to about twice its current size may be enough to get to the target. The team should treat that as a rough forecast, not as a promise, because it comes from extending the curve rather than from a direct test.

Key points:
- The target level is the benchmark being compared against.
- Extending the learning trend points to improvement from more examples.
- A doubling of the data appears likely to help enough.
- A projected trend is only an estimate, so it is not certain.

Rubric:
The response should link the projected meeting point with the benchmark to a plausible payoff from doubling the data and should clearly state that the conclusion is uncertain rather than guaranteed.

If the validation curve reaches the target after the data set doubles, what should be concluded?

Question:
Answer in one to three sentences: Why is a desired performance threshold drawn on a dev-error learning curve before extending the curve?

Sample answer:
The target line gives a clear benchmark for judging the projection. By comparing the extended curve with that benchmark, you can tell whether collecting more training data is likely to move the model close enough to the goal.

Key points:
- The target provides a benchmark.
- The projected curve can be compared with that benchmark.
- This helps estimate whether extra data may be worthwhile.

Rubric:
The answer should identify the target as a comparison line and connect it to deciding whether additional data could realistically reach the desired level.

Why Add the Target Line to a Learning Curve?

Which conclusion best matches the example learning curve?

A rough learning-curve estimate can support a tentative prediction about the value of adding more training data, but it does not guarantee the outcome.

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