I'm comparing a few algorithms, and trying to have them run using the same
random datasets.
Each algorithm is a separate python process and I provide a file with a list of
integers, generated using numpy.random.randint. It is a small sequence of
random integers between 0 and 10,000,000.
Every time a new run of a certain experiment is made, random_state is set to a
number in the seed sequence and fed into train_test_split() function.
Since I don't know about the internals of random_state behavior, I would like
to know whether this makes sense or not.
So far, I seem to be getting reasonable results, but it'd be great to have a
second opinion.
Thank you,
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