Validation set size for noticing a tiny accuracy change
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Typical Development Set Sizes for Tiny Accuracy Gains
High-Impact Business Systems May Justify a Larger Development Set
Formal significance tests for validation-set changes
What dev set size is most suitable for spotting a 0.1 percentage-point gain in accuracy?
A development set should always be expanded to the maximum possible size, even after it is already large enough to reveal meaningful performance changes.
Match each evaluation target with the dev set size it suggests.
Order the steps for deciding whether a dev set is large enough to detect a useful accuracy gain.
Match dev-set size to the smallest gain you care about.
Choose a dev set size that can detect a tiny but important gain.
Why is a 150-example dev set not enough to tell 83.0% from 83.4% accuracy?
When is a validation set much larger than 10,000 examples most justified?
If a validation set is already large enough to tell whether one model is meaningfully better than another, it does not need to be made much larger.
Sizing a Training Development Set
Accuracy Resolution on a 100-Example Development Set