Validation Set Size for Tiny Accuracy Gains
A holdout set with _____ examples is large enough that a 0.1% accuracy gain is usually noticeable.
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What dev set size is commonly suggested for detecting a 0.1% improvement in accuracy?
True or False: A validation set with 1,000 examples is enough to reliably detect a 0.1% improvement in accuracy.
Development sets with _____ to 10,000 examples are often considered common.
Which dev set size range is commonly used in practical machine learning projects?
True or False: A dev set with 10,000 examples can often reveal an accuracy gain of about 0.1 percentage point.
Validation Set Size for Tiny Accuracy Gains
Match each development-set idea to its best description.
Put the validation-set sizing logic in order.
What size of accuracy change is a 10,000-example dev set generally able to detect?
True or False: A development set with 1,000 examples is smaller than the commonly suggested range for spotting very small improvements.
Common development-set sizes begin at _____ examples.
Match each development-set situation to the most accurate classification.
Arrange the steps for deciding when a dev set must be large enough to notice a tiny metric gain.
Why Dev Set Size Matters for Detecting Small Gains
Selecting a Dev Set Size for a Small Accuracy Gain
Choosing a Development Set for Tiny Metric Gains