Why do labeling mistakes in a validation set matter more after a classifier gets stronger?
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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
Match each concept to its role when mislabeled development examples become more important.
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?