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0xFFFFFFFF is a large number; it's the hexadecimal representation of $232^{-1}$

0xFFFFFFFF is a large number; it's the hexadecimal representation of $2^{32}-1$

Based on the information I gave above, we see the crc32 function finds the checksum value in memory (of the unique identifier). If we know the unique ID has never changed, then We ensure that crc32(np.int64(identifier)) & 0xffffffff will always return exactly the same numeric value, across all python versions and platforms.

This simply applies our test_set_check function to each sample's unique identifiers. Using the apply methd on a Pandas Series object (here it is one column of a Pandas DataFrame).

3. In practice, do you commonly split datasets by id in this manner?

Not really... Scikit_learn's train_test_split if often good enough. I think there are many other ways to remove bias and errors from your models before worrying too much about the impact of random splits.

There is also bias in overfitting e.g. in sequential imaging data (think frames of videos) such that the background is consistent, even though the objects you might want to detect are not. Your model will learn what to expect based on the background, which is not robust! HEre you might look into using a geographical split (i.e. not random at all).

Based on the information I gave above, we see the crc32 function finds the checksum value in memory (of the unique identifier). If we know the unique ID has never changed, then We ensure that crc32(np.int64(identifier)) & 0xffffffff will always return exactly the same numeric value, across all Python versions and platforms.

This simply applies our test_set_check function to each sample's unique identifiers. Using the apply methd on a Pandas Series object (here it is one column of a Pandas DataFrame).

3. In practice, do you commonly split datasets by ID in this manner?

Not really... Scikit-Learn's train_test_split is often good enough. I think there are many other ways to remove bias and errors from your models before worrying too much about the impact of random splits.

There is also bias in overfitting e.g. in sequential imaging data (think frames of videos) such that the background is consistent, even though the objects you might want to detect are not. Your model will learn what to expect based on the background, which is not robust! Here you might look into using a geographical split (not random at all).

In this part: test_ratio * 2**32, the part $2^{32}$ represent the largest integer we are checking that the

What is the anonymous function doing in the 2nd to last line?

lambda id_: test_set_check(id_, test_ratio)

In practice, do you commonly split datasets by id in this manner?

In this part: test_ratio * 2**32, the part $2^{32}$ represents the largest integer of a 32-bit system.

2. What is the anonymous function doing in the 2nd to last line?

lambda id_: test_set_check(id_, test_ratio)

3. In practice, do you commonly split datasets by id in this manner?