That's really encouraging to see my research ideas getting into
sklearn! Thanks so much for taking the time to do this :)

I'm definitely interested to hear how it works for people in various
applications. I think that generally for 3 or more dimensions, random
search should match or exceed grid search.

I'd like to also add that for applications with maybe <= 5 dimensions,
it might be best to use some of the quasi-random sequences like Sobol
or Niederreiter or something. I'm not sure what is the best way to
access these things from Python though. The GSL has them, but the
license is too strict right?  I had hoped to find them in
scipy.integrate, but I don't see any of those kinds of sequences
there. I see that in [1] there are some interesting LGPL codes, can
they be brought into scipy?

[1] http://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html

On Sat, Oct 6, 2012 at 7:37 AM, Andreas Mueller
<[email protected]> wrote:
> Hi everybody.
> Last weekend I created a draft of a random hyper parameter search a la
> James.
> It doesn't do tree-structured parameter spaces and doesn't do the Parzen
> estimate
> (yet), just random sampling.
> Feedback would be very welcome, as I want to use this as a basis for a
> better grid search
> visualization PR.
> You can find my PR here:
> https://github.com/scikit-learn/scikit-learn/pull/1194
>
> Cheers,
> Andy
>
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