Satrajit,
Say hi to Raphael for me! He's actually one of my colleagues and
Pierre Geurts (the author of extra-trees) is his advisor and mine as
well :-)
Maybe you missed that, but we talked about this topic on the current
pull request [1]. It has been marked as a future enhancement, but I
plan to
i was recently introduced to the following random forests implementation at
a workshop:
http://orbi.ulg.ac.be/bitstream/2268/9357/1/geurts-mlj-advance.pdf
we might want to keep these variants in mind as we move forward.
cheers,
satra
-
Thanks to combined work with Stefan and Janto Dreijer, the scikit-learn
is still on the scikits web page, even after the renaming:
http://scikits.appspot.com/scikits
Hurray!
Gael
--
All the data continuously generated in
On 22 September 2011 19:38, Vlad Niculae wrote:
> It was a huge pleasure to play a part!
>
> Vlad
>
> On Thu, Sep 22, 2011 at 12:14 PM, Fabian Pedregosa
> wrote:
> >
> >
> > On Thursday, September 22, 2011, Gael Varoquaux wrote:
> >>
> >> A new release of awesome!
> >>
> >> > http://scikit-lea
It was a huge pleasure to play a part!
Vlad
On Thu, Sep 22, 2011 at 12:14 PM, Fabian Pedregosa
wrote:
>
>
> On Thursday, September 22, 2011, Gael Varoquaux wrote:
>>
>> A new release of awesome!
>>
>> > http://scikit-learn.sourceforge.net/whats_new.html
>>
>> Pretty crazy changelog. Cheers to
On Thursday, September 22, 2011, Gael Varoquaux wrote:
> A new release of awesome!
>
> > http://scikit-learn.sourceforge.net/whats_new.html
>
> Pretty crazy changelog. Cheers to the team!
>
> Thanks Fabian (and I believe Vlad) for making the release. Releases are
> vital for a project.
>
Indeed
A new release of awesome!
> http://scikit-learn.sourceforge.net/whats_new.html
Pretty crazy changelog. Cheers to the team!
Thanks Fabian (and I believe Vlad) for making the release. Releases are
vital for a project.
Gael
---
Dear all,
I am pleased to announce the availability of scikits.learn 0.9.
scikit-learn 0.9 was released on September 2011, three months after the 0.8
release and includes the new modules Manifold learning, The Dirichlet
Process as well as a dozen of new algorithms, datasets, performance and
docum