On 02/23/2012 02:37 PM, Adrien Gaidon wrote:
>
> Some time ago, I had sent a quick fix using the second approach Matthieu
> mentioned. I've
> been using it successfully in my own code for some time.
>
> I cleaned it up a little bit and added a test. The pull request is here:
> https://github.com
Hi,
2012/2/23 Mathieu Blondel
> On Thu, Feb 23, 2012 at 12:53 PM, Gael Varoquaux
> wrote:
>
> > I don't think so. It is clearly a flaw of the current design. Anybody who
> > has an elegant solution to proposed will be greeted with much rejoicing
>
> We could add a squared_matrix=True|False opti
On Thu, Feb 23, 2012 at 12:53 PM, Gael Varoquaux
wrote:
> I don't think so. It is clearly a flaw of the current design. Anybody who
> has an elegant solution to proposed will be greeted with much rejoicing
We could add a squared_matrix=True|False option to the constructor of
GridSearchCV or we c
Hi,
I only recently started using sklearn and it's an impressive and well
documented library. Thanks!
I run into some strange behavior while using the function
'permutation_test_score'.
When using permutation_test_score with n_permutations = 50, everything
looks alright
In [4]: cv_scores, perm
On Thu, Feb 23, 2012 at 12:07:54PM +0100, Emanuele Olivetti wrote:
> Is there a ready-made solution to this?
I don't think so. It is clearly a flaw of the current design. Anybody who
has an elegant solution to proposed will be greeted with much rejoicing
:).
G
---
Hi,
While playing with GridSearchCV and an SVM with precomputed kernel
I came across the issue that cross-validation seems not to slice
the precomputed kernel in the proper way during fit(). Here is a
minimal example:
---
import numpy as np
from sklearn.grid_search import GridSearchCV
from sklear
