Match each concept to its role when mislabeled development examples become more important.
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Why do labeling mistakes in a validation set matter more after a classifier gets stronger?
A small number of mislabeled validation or test examples may be acceptable at first, and that decision can be revisited later.
When Label Cleanup Becomes Worth the Effort
Match each development-set scenario with its implication for mislabeled examples.
Order the steps for deciding whether label cleanup on a development set is worth the effort.
A speech-recognition dev set has about 3% error, and 40% of those errors come from incorrectly transcribed examples. What should you do?
A model error rate of 1.4% versus 2.0% is a small difference that usually does not matter.
How mislabeled dev examples matter after the model gets better
Order the stages showing why mislabeled development examples matter more as a classifier improves.
When and why mislabeled development examples become more costly to ignore
Decide whether to relabel the dev set once label noise becomes a large share of remaining errors.
Why does noisy dev-set labeling matter more as a model improves?