On 01/25/2012 10:09 AM, Mathieu Blondel wrote:
> On Wed, Jan 25, 2012 at 3:03 PM, Mathieu Blondel wrote:
>
>
>> I will do it later today.
>>
> Done in
> https://github.com/scikit-learn/scikit-learn/commit/77d83b61f9161899de286ca09601aa648e9c31ff.
>
Thanks!
Will try it later :)
Andy
On Wed, Jan 25, 2012 at 3:03 PM, Mathieu Blondel wrote:
> I will do it later today.
Done in
https://github.com/scikit-learn/scikit-learn/commit/77d83b61f9161899de286ca09601aa648e9c31ff.
Mathieu
--
Keep Your Developer
CoefSelectTransformerMixin must be deprecated or deleted (I would
favor the latter as I guess nobody uses it in user-land code) and
replaced in favor of SelectorMixin. I will do it later today.
https://github.com/scikit-learn/scikit-learn/issues/518
Mathieu
--
On Tue, Jan 24, 2012 at 11:07:20PM +0100, Andreas wrote:
> Taking the mean means that if a feature has a strong positive weight for one
> class and a strong negative weight for another class, they might cancel,
> leading to the feature being not present in the solution.
> Why does that make sense?
Hi everybody.
At the moment I'm trying to understand feature selection.
I was looking at the "L1 based feature selection" that is described in
the docs.
I was trying to use that with LinearSVC but I don't really understand
what is
going on. Maybe someone can explain.
I am in the mult-class setu
