Why should source-mixed examples be kept out of both the development and test sets?

True or False: If your dataset mixes records from several sources with noticeably different distributions, the best practice is to randomly shuffle everything before creating the train, dev, and test splits.

For a movie-recommendation system, the dev and test sets should match the _____ of the users' future ratings, not a randomly shuffled archive.

If 188,000 website logs and 12,000 mobile-app logs are combined and then randomly split into dev/test sets, what fraction of the dev/test data will come from website logs?

Source-mix effect in a random split

Match each dev/test-set design choice to the effect it has on model development.

Put the reasoning steps in order for deciding whether mixed-source data should be randomly split into dev/test sets.

Selecting Validation and Test Data

If a dataset is formed by randomly mixing 4,000 photos from a mobile app with 156,000 photos collected from the web, the validation and test sets will reflect the mobile-app distribution.

Choose dev and test sets that match the data you expect to see in the _____.

Match each data-partitioning situation to the recommended outcome.

Order the steps for splitting mixed-source data so evaluation reflects the target distribution.

Analyze the consequences of mixing greenhouse and orchard photos before creating the dev and test sets.

Check a split strategy for a plant-disease app trained on mixed-source photos.

Why is a mixed dev/test split misleading in a warehouse-vision project?