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
Training Set Error Is Usually Lower Than Dev Set Error
Why plot training error when judging whether more data might improve a model?
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 _____.
Match each learning-curve element to its use when thinking about the value of additional data.
Arrange the steps for judging whether more data may help a model.
How does training error help assess the value of more data?
Estimating the Benefit of More Data
Why add a training-error curve?
What extra plot helps judge the value of collecting more data?
Training loss can help interpret a validation-loss curve when estimating the value of more data.
Learn After
How do training error and dev error typically compare for a fitted model?
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?
In a typical learning curve plot, how do training error and dev error usually compare?
Typical Relationship Between Training and Development Error