Learn Before
Using Training Error to Judge the Value of More Data
If you only inspect the development error curve, it may be difficult to estimate how performance will change as the training set grows. A training-error curve provides another signal that can help assess whether additional data is likely to improve the model.
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Related
Constructing a Performance Curve from Different Training Set Sizes
Larger Training Sets Usually Lower Dev-Set Error
Target Error Rate on a Learning Curve
Using a Development-Set Curve to Estimate the Payoff of More Data
Using Training Error to Judge the Value of More Data
Why plot training and development error together on a learning curve?
Tiny Training Samples Make Learning Curves Unstable
What are the axes of a learning curve?
A learning curve shows error versus network depth.
Learning Curves and Validation Error
Parts of a learning curve
Constructing a learning curve for model selection
What a learning curve shows
Using a learning curve to judge whether more data will help
What a Learning Curve Shows
What belongs on the vertical axis of a learning curve?
A learning curve is less informative than a single validation score.
Learn After
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.