That should work but seems a bit complicated.
Do you want multiple random datasets and multiple train_test_splits?
Instead of multiple train_test_splits you could just use a deterministic
cross-validation, for example.
Also, if you want a deterministic but varying way to set the random
seed, why not just use a range?
On 02/16/2015 10:25 PM, Pagliari, Roberto wrote:
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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