What limitation motivates adding a training-error plot?
Question: Answer in one to three sentences: What problem with the dev-error curve motivates plotting training error?
Sample answer: The dev-error curve by itself can be hard to extrapolate, making it difficult to predict exactly where it will go with more data. Training error is added to help estimate the impact of adding data.
Key points:
- Dev error alone can be hard to extrapolate.
- Training error helps estimate the impact of more data.
Rubric: The answer should mention difficult extrapolation from dev error alone and identify training error as the additional plot used to support the estimate.
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Related
Training Error Usually Increases with Training Set Size
Training Error Is Usually Lower Than Dev Error
Why should training error be plotted when evaluating whether more data may help?
A dev-error curve alone may be difficult to extrapolate to larger training sets.
The additional plot used to estimate the effect of more data is _____.
Match each learning-curve element to its role in estimating the effect of more data.
Order the reasoning process for assessing the possible impact of adding data.
Explain why training error strengthens an analysis of whether more data may help.
A team cannot confidently project its dev-error curve. What analysis should it add?
What limitation motivates adding a training-error plot?
Which analysis best follows the source when considering a larger dataset?
Training error is intended to supplement, rather than replace, the dev-error curve.