Thanks Gael,
Yes, I've been thinking a lot about density estimation, and I've
designed all the astroML code to be fairly easy to move upstream if
desired. I have a bit of a vision for density estimation: I'd love in
the future to create an sklearn.density submodule which has things like
KDE (built on an improved ball tree), KNN density, Extreme
Deconvolution, etc. They'd have an interface similar to the current GMM
(most of that code, as you saw, is already in astroML).
When that is in place, we could create a very general Bayesian
generative classifier, which would learn a density representation for
each class using any of these estimators, allow for user-specifiable
priors, and then perform probabilistic classification of new points
based on the per-class densities. This would supersede GaussianNB,
KNeighborsClassifier, and RadiusNeighborsClassifier (and maybe others),
in the sense that they could be easily implemented as specializations of
the new routine. I think this could be a really powerful addition to
scikit-learn.
Just my thoughts for the morning... back to PyData!
Jake
On 10/27/2012 01:17 AM, Gael Varoquaux wrote:
> It looks really awesome! The examples are superbe.
>
> It looks like you have some really cool density estimation code. I would
> personnally love to see such functionality in the scikit. Do you think
> that some of it could be move upstream?
>
> Thanks a lot for being our astrophysics figure-head! I feel that the
> astroML and the scikit will have an impact there.
>
> Gael
>
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