What does a validation-error curve above the training-error curve mean?
Question: Answer in one to three sentences using the usual relationship between training and development errors.
Sample answer: It means the development error is larger than the training error. The model is doing better on the training examples than on the development examples, which suggests weaker generalization.
Key points:
- Development error is higher than training error.
- The model fits the training set more closely than the development set.
Rubric: The answer should mention both the higher development error and the model's better performance on the training set.
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Related
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.
In a typical learning curve plot, how do training error and dev error usually compare?
Typical Relationship Between Training and Development Error
Related
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.
In a typical learning curve plot, how do training error and dev error usually compare?
Typical Relationship Between Training and Development Error