@Olivier, the quick reproduction of the error using 20Newsgroups -
https://gist.github.com/1372557
Also, does it mean, actually, for text classification problems, trees are
used less often?
@Mathieu, is this the case only for Ridge? kNN, NB, linearSVC do not have
such a behavior.
If for Ridge, different solvers are used, which result should I refer to as
result from Ridge?
Thanks a lot for your kind help.
On 17 November 2011 07:33, Mathieu Blondel <[email protected]> wrote:
> On Thu, Nov 17, 2011 at 1:54 AM, SK Sn <[email protected]> wrote:
>
> > The difference of results (f1/precision/recall) between X sparse and
> > (X.todense() or X.array()) are about -0.5% to +1.0%.
>
> The difference comes from the fact that different solvers are used for
> sparse matrices and numpy arrays.
>
> Mathieu
>
>
> ------------------------------------------------------------------------------
> All the data continuously generated in your IT infrastructure
> contains a definitive record of customers, application performance,
> security threats, fraudulent activity, and more. Splunk takes this
> data and makes sense of it. IT sense. And common sense.
> http://p.sf.net/sfu/splunk-novd2d
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
All the data continuously generated in your IT infrastructure
contains a definitive record of customers, application performance,
security threats, fraudulent activity, and more. Splunk takes this
data and makes sense of it. IT sense. And common sense.
http://p.sf.net/sfu/splunk-novd2d
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
