I have some familiarity with the GP stuff in sklearn, but one of the big
things I really *want* is something much more like George - specifically a
HODLR solver. Maybe it is outside the scope of the project, but I think GPs
in sklearn could be very useful and computationally tractable for "big-ish"
data
I agree with Gael on all counts of a) numerical questions b) API questions.
I am also dubious that the current handling of kernels is the "right way"
to do things, and there are a number of small and big GP fixes that are
open PRs.
One very specific reason to focus on GP code quality would be that it opens
the door to use sklearn's own code to implement some very nice
hyperparameter search algorithms which could be useful to many users.
On Tue, Nov 25, 2014 at 12:18 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Tue, Nov 25, 2014 at 12:13:53PM -0500, Andy wrote:
> > the way that is done in GPy? Or should we leave that to GPy? Then the
> > question is how useful our implementation is without it :-/
>
> Wait a second. Our implementation is crap. That is clear. It's is still
> very useful. Claiming that it is not useful is (pardon my words)
> ridiculous. Many people have simple needs that don't require kernel
> engineering.
>
> Now it may be a good idea to add kernel engineering to our GPs. I won't
> dispute that. But even before we do this, I have in mind that the code we
> have is hard to follow, and possibly numerically unstable. It may need
> work on the quality of the code before we add features.
>
> Gaël
>
>
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