Related: We should probably raise an exception if more than one of
score_func, loss_func and scoring is passed. I don't understand how
score_overrides_loss fits in...
On Wed, Jul 31, 2013 at 1:50 AM, Lars Buitinck wrote:
> 2013/7/30 Wifi Gi :
> > score = cross_validation.cross_val_score(alg, x,
On 07/30/2013 06:00 PM, Wifi Gi wrote:
> Lars,
> Thanks, that seems to work. I am a bit confused though - the
> help(cross_validation.cross_val_score) command specifically says the
> "scoring" argument can be either a string or a callable (description
> copied here):
> scoring : string or calla
ed the feature selector, the information is lost. Sorry
>
> G
>
>
>
> --
>
> Message: 3
> Date: Mon, 29 Jul 2013 18:32:07 +0200
> From: Olivier Grisel
> Subject: Re: [Scikit-learn-general] Finding the Features a Model Used?
> To: scikit-learn-general
> Mes
2013/7/30 Wifi Gi :
> score = cross_validation.cross_val_score(alg, x, y, cv=folds,
> scoring=metrics.accuracy_score)
You're mixing scoring (the new API) with score_func (the old API
described in the 0.13.1 docs). Use `scoring="accuracy"`.
--
Lars Buitinck
Scientific programmer, ILPS
University
On 07/30/2013 05:35 PM, Wifi Gi wrote:
> I'm having trouble figuring out how to use the cross validation
> scoring function. As far as I can tell, I'm exactly following the
> tutorial given here:
> http://scikit-learn.org/0.13/modules/cross_validation.html#computing-cross-validated-metrics
>
>
>
I'm having trouble figuring out how to use the cross validation scoring
function. As far as I can tell, I'm exactly following the tutorial given
here:
http://scikit-learn.org/0.13/modules/cross_validation.html#computing-cross-validated-metrics
Paraphrase of code:
from sklearn import naive_bayes
f
