What is the main problem with randomly shuffling user and internet images together into dev/test sets for a cat app?

True or False: Randomly shuffling all available data into train/dev/test sets is recommended when your data sources have different distributions.

Dev and test sets should be chosen to reflect the _____ you expect to encounter in the future, not the overall shuffled data pool.

In the cat-app example, what percentage of dev/test data comes from internet images if all 210,000 images are randomly shuffled?

Andrew Ng recommends randomly shuffling all available data into dev/test sets even when the data sources differ from the target distribution.

In the cat-app example, randomly shuffling all data means about _____% of dev/test images would come from internet sources.

Match each dev/test set characteristic to the consequence it produces for the ML team.

Order the reasoning steps that explain why randomly shuffling mixed-source data into dev/test sets is problematic.

According to Andrew Ng, what is the primary criterion when choosing dev and test sets?

Randomly shuffling 5,000 user images with 205,000 internet images produces a dev/test set that accurately reflects the app-user distribution.

Andrew Ng recommends choosing dev and test sets to reflect data you expect to get in the _____ and want to do well on.

Match each data-partitioning scenario to the corresponding recommendation or outcome from Machine Learning Yearning.

Order the steps for correctly partitioning mixed-source data so that dev/test sets reflect the target distribution.

Analyze the consequences of shuffling mixed-source data into the dev and test sets.

Evaluate the data partitioning strategy for a cat-app using mixed-source data.

Why must dev/test sets reflect the target distribution instead of a shuffled mix?