What is the most likely explanation if a model is accurate on the examples it was trained on but much less accurate on a separate development subset from the same source?

A speech-recognition system is trained on clean studio speech, and both the training set and the development set include a 'noisy train station' slice. What should you measure separately on that slice?

What Kind of Data Should Be Added After a Train–Dev Gap?

If both the training set and the training-dev set contain dashcam audio from city buses, what is the best first step for checking a data mismatch hypothesis?

A performance gap on the same minority slice supports the data-mismatch hypothesis.

A gap between training and the _____ usually signals overfitting.

Match each observation about a subway-audio subset to what it suggests about the distribution mismatch question.

Order the diagnostic steps for testing whether a mismatch between data sources is the problem.

What should you conclude if a speech model is accurate on studio-recorded clips in the training set but much less accurate on studio-recorded clips in the training dev set?

If a fraud detector performs well on mobile-app purchases that appear in both the training set and the training-dev set, but poorly on in-store purchases that appear only in the training-dev set, this evidence cannot make a data-mismatch hypothesis more plausible.

Check the Matching Data Subset

Match each term in the distribution-mismatch diagnostic process to its correct meaning.

Order the steps for checking whether more conference-room audio is likely to help a speech model.

Using subset comparisons to test whether more targeted data is needed

Checking a Data Mismatch in Loading-Dock Speech

Validating the Case for More Data from a Specific Subset