Why Different Dev and Test Distributions Complicate Diagnosis
When the development set and the test set come from different distributions, a model can look strong on development data and weak on test data for more than one reason. The main possibilities are that the model overfit the development set, the test set is genuinely harder, or the test set reflects a different distribution rather than a more difficult one.
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If a model is tuned on a development set and then performs worse on a separate test set, even though both sets come from the same source distribution, what is the most likely explanation?
True or False: If validation and test data are drawn from different populations, a score gap between them always has one clear cause.
If a model has started fitting the validation set too closely and the training and validation data come from the same distribution, the usual remedy is to get more _____ data.
Why should the development set match the main goal of the project?
When validation and test data come from the same distribution, a strong validation score followed by a much weaker test score suggests overfitting to the validation set.
If the development set is being overused and the training and development data come from the same distribution, the practical fix is to collect more _____ data.
Match each development-and-test-set situation with its consequence for debugging.
Order the troubleshooting steps when validation performance is strong but holdout performance is weak.
Why a Test Set Can Score Lower Than a Dev Set
A test score from a different data distribution tells you exactly why the model failed.
After the development and test sets are set, the team will spend its effort improving _____ set performance.
Match each concept about dev and test set distributions to the correct description.
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Explain what poor final performance means when the tuning set and final set do or do not match.
Why a model can look strong in development but weak in final evaluation
What is the diagnosis and remedy when test results lag far behind dev results under the same data distribution?
Learn After
Why a Mismatched Validation Set Can Waste Effort
Which explanation is NOT one of the standard reasons a model can score well on the dev set but poorly on the test set when the two sets come from different distributions?
True or False: If a development set and a test set come from different distributions, it is easy to pinpoint the reason a model scores worse on the test set.
If the development set and the test set come from different _____, a performance gap is hard to interpret.
Match each distribution-shift situation to the most likely explanation.
Order the three explanations for why a model can look strong on development data but weak on test data when the data distributions are different.
When the test data are drawn from a tougher distribution than the development data, what is the most reasonable conclusion?
True or False: If a model scores lower on the test set than on the dev set, then the test set must be inherently more difficult.
A model may do well on the _____ set and still perform poorly on the test set if the two sets come from different distributions.
Match each dev/test diagnosis to the most appropriate next step.
Arrange the steps a practitioner follows when a spam filter succeeds on validation data but fails after deployment.
Why a good development score can still leave test results unexplained
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