Firstly, yes, fit_grid_point is being replaced cross_val_score. It wants
you to review it! https://github.com/scikit-learn/scikit-learn/pull/2736
Secondly, my prior proposals include:
- On the mailing list in March 2013 I suggested a minimal code change
although not very user friendly appro
I'd definitely like to have support for multiple metrics. My use case is
that I have several methods that I want to evaluate against different
metrics and I want the hyper-parameters to be tuned against each metric. In
addition I don't have a test set so I need to use cross-validation both for
eval
On Mon, Jan 13, 2014 at 5:09 PM, florian.wilh...@gmail.com <
florian.wilh...@gmail.com> wrote:
>
> So setting epsilon=0 and C to a large value should result in a
> regression in the L1 norm with almost no regularization of w, right?.
> One thing that just crossed my mind. Would it be possible in a
@Alexandre, @Mathieu: Thanks for these hints. I'll give it a try.
So setting epsilon=0 and C to a large value should result in a
regression in the L1 norm with almost no regularization of w, right?.
One thing that just crossed my mind. Would it be possible in a linear
SVR setting to let the norm(w
