Training Set Error Is Usually Lower Than Dev Set Error
A learning system is usually optimized to fit the training examples it sees during learning, so it typically makes fewer mistakes on the training set than on the development set. As a result, the dev-error curve is usually above the training-error curve.
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More Data Often Raises Training Error
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A development-error curve computed by itself can be hard to project to much larger training sets.
The extra plot used to judge whether more data will help is _____.
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Estimating the Benefit of More Data
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Training loss can help interpret a validation-loss curve when estimating the value of more data.
Training Set Error Is Usually Lower Than Dev Set Error
Learn After
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Validation error is usually higher than training error.
A model often makes fewer errors on the _____ set.
Match each plot element with its usual meaning.
Put the usual training-versus-validation error reasoning in order.
Why the validation-error curve is usually above the training-error curve
Read a chart where the validation-error line sits above the training-error line.
What does a validation-error curve above the training-error curve mean?
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Typical Relationship Between Training and Development Error