Hi Andy,
Thanks for the comments. Yes I could use range as well. I just don't know how
that works internally, and preferred to provide a vector of random numbers.
In any case, thank you for the comments.
From: Andy [mailto:t3k...@gmail.com]
Sent: Tuesday, February 17, 2015 6:31 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] random splitting and random seed
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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