Training–Dev Distribution Gap
A training–dev distribution gap happens when a model handles fresh examples that come from the same distribution as the training data, but performs much worse on the dev/test data. The usual reason is that the training examples are not a good representation of the dev/test examples.
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Which data should define the dev and test sets for the plant-disease app?
Development and test examples should be drawn to match the kind of data the deployed system is expected to see.
Development and test sets should match the kind of data you expect later.
Match each data group for the plant-disease app with its role.
Order the dataset choices when training data and evaluation data come from different sources.
Why training data and evaluation data may come from different sources
Find the evaluation-set mismatch in a voice-command detector.
Why did strong desktop-photo results not predict mobile-upload success?
Choosing Training and Evaluation Data from Two Image Sources
If training uses extra web-sourced photos, the dev and test sets must also contain web-sourced photos.
Learn After
Recognizing Distribution Mismatch from Error Measurements
Collect Training Examples That Resemble the Hard Development Cases
Compare training and development data properties after a mismatch is found
What best describes a mismatch between training data and evaluation data?
A data mismatch problem means the model performs poorly on both training-like data and the development/test data.
Data mismatch and distribution fit
Match each data-distribution term with its description.
Put the diagnostic workflow for dataset shift in the correct order.
What most directly causes a mismatch between training data and evaluation data?
A voice-to-text system can perform well on the training set and training-dev set but still do poorly on the dev set if the dev data comes from a different distribution.
Training Distribution and Evaluation Distribution
Match each performance pattern to the most likely diagnosis of the model's problem.
Put the steps in order for diagnosing a distribution mismatch from model performance.
Explain what a performance gap across two data distributions suggests
Explain why a voice-command model looks strong on one split but weak on another.
Why is this called data mismatch?