For which dataset size is the 70/30 train/test split heuristic most appropriate?

True or False: As ML datasets grow to billions of examples, the fraction of data allocated to dev/test sets should grow proportionally to maintain the 70/30 split.

For large-scale ML problems, the _____ of data allocated to dev/test sets has been shrinking even as the absolute number of examples grows.

For which example-count range does the 70/30 train/test split heuristic work well, according to Andrew Ng?

As total dataset size grows into the billions, the fraction of data allocated to dev/test sets also increases.

The 70/30 train/test heuristic works well when you have a _____ number of examples, such as 100 to 10,000.

Match each dataset scale or concept to the correct dev/test sizing implication from Machine Learning Yearning.

Order the reasoning steps Ng uses to argue that the 70/30 heuristic does not apply to large datasets.

In big-data ML problems, what happens to the absolute number of dev/test examples as total dataset size grows?

Dev/test sets should be no larger than what is needed to evaluate the performance of your algorithms.

One popular heuristic was to use _____ of your data for your test set, though this does not apply to large datasets.

Match each train/dev/test splitting term to its correct definition from Machine Learning Yearning.

Order the steps a practitioner should follow when deciding test set size as their dataset grows from thousands to billions of examples.

Why does the traditional 70/30 train/test split heuristic fail to scale efficiently for big-data machine learning problems?

Evaluating dev and test set sizes for a billion-scale image classification system

Determining dev/test set size in the era of big-data machine learning