Many thanks to all contributors, and especially to Andreas !
Best,
Bertrand
- Mail original -
> De: "Andreas Mueller"
> À: "scikit-learn-general"
> Envoyé: Vendredi 6 Novembre 2015 01:45:18
> Objet: [Scikit-learn-general] [ANN] Scikit-learn 0.17 released
>
> Hey everybody.
>
> I'm hap
Fantastic ! thanks,
Bertrand
- Mail original -
> De: "Olivier Grisel"
> À: "scikit-learn-general"
> Envoyé: Mercredi 13 Avril 2016 23:52:22
> Objet: Re: [Scikit-learn-general] Binary wheel packages for Linux are coming
>
> OpenBLAS 0.2.18 has been released yesterday with many fixes se
- Mail original -
> De: "Adelina Grant"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Mardi 14 Janvier 2014 17:34:22
> Objet: [Scikit-learn-general] Scoring multiple test samples together
> rather than separately
> Hi
> I built a GMM classifier with the scikit learn package
Impressive. These are long-standing issues. Thanks for the patches.
Bertrand
- Mail original -
> De: "Manoj Kumar"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Vendredi 22 Août 2014 01:35:13
> Objet: [Scikit-learn-general] [GSoC] Wrap up post
> Hi,
> A quick wrap up pos
Great ! Many thanks to Olivier and Andy !
Best,
Bertrand
- Mail original -
> De: "Gael Varoquaux"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Vendredi 27 Mars 2015 18:22:30
> Objet: Re: [Scikit-learn-general] [ANN] scikit-learn 0.16.0 is out!
>
> Congratulations Olivier an
Regarding clustering algorithms, I would suggest to have a look at convex
formulations, that can be advantageous for the sake of convergence/stability,
wrt standard algorithms that never have any guarantee. Among others: -
http://www.icml-2011.org/papers/419_icmlpaper.pdf -
http://www.google.fr
> The current idea would be to use n_clusters for all clustering
> algorithms and n_components
> for GMM.
+1
B
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If you use a cross validation scheme, where you estimate the residuals variance
on left-out data and compare it to the variance of the model with the intercept
only, then R^2 can be negative. This approach is an alternative to adjusted R^2
for model selection, and probably makes more sense when
As alluded previously, you need to find a way to compute the centroid by
minimizing the sum of squared distances to a given set of points within each
cluster. However, it is true that re-projecting the euclidean mean to the
sphere would approximate well the theoretical solution in most cases.
- Mail original -
> De: "Andreas Mueller"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Jeudi 14 Mars 2013 13:30:34
> Objet: Re: [Scikit-learn-general] PyCON Australia 2013
>
> Btw, don't get me wrong, I think it is great what INRIA and Gael's
> team to.
> But singling them
> It would be nice to clarify if INRIA is still paying an engineer for
> the project and if so whether he's full-time or has other duties.
Yes, INRIA has been paying a full time engineer (Fabian, then Jaques) on the
project since January 2010.
Bertrand
-
- Mail original -
> De: "Jacob Vanderplas"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Dimanche 7 Juillet 2013 19:10:38
> Objet: [Scikit-learn-general] Defining a Density Estimation Interface
> Hi,
> I've been working on a big rewrite of the Ball Tree and KD Tree in
> sklea
- Mail original -
> De: "Skipper Seabold"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Lundi 8 Juillet 2013 19:40:36
> Objet: Re: [Scikit-learn-general] Defining a Density Estimation Interface
>
> On Mon, Jul 8, 2013 at 1:20 PM, Bertrand
Dear all,
In the sklearn.metrics module, the precision_recall_fscore_support and
precision_score have different default value for the "average" parameter, which
seems a bit counterintuitive -- but I did not go deep into the code. Is this
really the intended behavior ?
def precision_score(y_t
Congratulations !
Bertrand
- Mail original -
> De: "Gael Varoquaux"
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Jeudi 8 Août 2013 01:18:09
> Objet: [Scikit-learn-general] Release 0.14: tagged and pushed!
>
> Hi Scikiters,
>
> I have tagged and pushed release 0.14.
>
> Vl
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