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?

If a model will be judged on roadside camera footage from one region, it is best to randomly mix together examples from several different regions when creating the dev and test sets.

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?