True or false: If a model performs well on the training set but poorly on the dev set, a useful next step is to compare how the two datasets differ.
This kind of comparison can reveal whether the evaluation data comes from a different distribution than the training data.
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Using Error Analysis to Investigate Dataset Mismatch
What Should You Do First When Training and Development Data Do Not Match?
True or false: If a model performs well on the training set but poorly on the dev set, a useful next step is to compare how the two datasets differ.
What should you compare when diagnosing a data mismatch?
When a training set and a validation set look different, what should you examine first?
When a model performs differently on two datasets from different populations, a useful first step is to identify which properties of the examples differ between the two sets.
When a train-dev mismatch is suspected, the key diagnostic is to compare which _____ differ across the two datasets.
Match each data-split term to its meaning in diagnosing distribution shift.
Put the steps for identifying a training-development mismatch in order.
Why compare training-set properties with dev-set properties when a model shows a mismatch problem?
True or False: A model that scores well on training data but much worse on dev data is probably underfitting.
A gap between _____ performance and dev-set performance can signal data mismatch.
Match each observation or action to its correct interpretation in a data mismatch diagnosis.
Order the reasoning for diagnosing a training-dev mismatch
How to investigate a dataset mismatch after a dev-set drop
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