More Data Often Raises Training Error
As the training set grows, development and test error usually move downward, but the error measured on the training set often moves upward. With a very small dataset, a model can often memorize the examples and achieve nearly perfect training performance. When the dataset becomes larger, more varied, or noisier, fitting every example exactly becomes harder, so training error tends to increase.
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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
What usually happens to training error as the training set becomes larger?
A very small training set can make a model seem impressively accurate on training data.
Training Error and More Data
Match each training-data situation with its typical effect on error.
Order the reasoning that explains why training error can increase.
Why can training error go up even as dev error goes down?
Explain the error trend after expanding a dataset
Why might a model get 0% training error on only two samples?
Which learning-curve trend is most typical when the training set gets larger?
Training error must always stay the same or decrease as the training set gets larger.