When Is a 70/30 Train/Test Split Reasonable?

True or False: If a project grows from 50,000 examples to 50 million examples, the dev and test sets must also grow by the same proportion to keep the split at 70/30.

In very large ML projects, the _____ of examples reserved for dev and test is often much smaller than the old 70/30 rule would suggest.

For what size of dataset is the simple 70/30 train/test split rule most suitable?

When a dataset grows to billions of examples, the percentage reserved for dev and test sets should also increase.

Connect each scale or idea to the best rule for choosing dev and test sets.

Order the steps for deciding dev/test set size when a dataset is very large.

Large datasets and the size of dev/test sets

A dev set should be large enough to estimate model performance reliably, but not larger than necessary for that purpose.

A common old rule was to reserve _____ of the data for testing, although that rule is not suitable for very large datasets.

Match each split-size concept to its meaning.

Order the actions a team should take when choosing test-set size as a dataset expands from thousands to tens of millions of examples.

How should holdout splits change when a dataset becomes very large?

Setting Holdout Sizes for a Very Large Image Dataset

How dev and test set sizes change when datasets become very large