I'm trying to use cross_val_score inside a lambda function to take full
advantage of my processors - especially because previously I was having
problems when setting cross_val_score's built in n_jobs > 1. I have
successfully done something similar before, though in a little bit
different way, so I
On 10/01/2014 04:23 PM, Gavin Hackeling wrote:
Hi all,
I am working on an character recognition problem with the Chars74K
data set. I am reshaping the images to 30x30 pixels, and using the 900
pixels' intensities as features. I am classifying the images using a
SVC with an RBF kernel.
...
On 10/03/2014 11:10 AM, Olivier Grisel wrote:
> 2014-09-27 4:51 GMT+02:00 Mathieu Blondel :
>> This is because LinearSVC doesn't support sample_weight.
>>
>> I added a new issue for raising a more explicit error message:
>> https://github.com/scikit-learn/scikit-learn/issues/3711
>>
>> BTW, a linea
2014-10-03 13:55 GMT+02:00 Mathieu Blondel :
> If you want to use the exponential loss (the loss used by AdaBoost), you can
> train a (single) linear model which minimizes it directly. The main point I
> want to make is that a LinearSVC is not a good choice of weak learner.
Alright.
--
Olivier
h
If you want to use the exponential loss (the loss used by AdaBoost), you
can train a (single) linear model which minimizes it directly. The main
point I want to make is that a LinearSVC is not a good choice of weak
learner.
M.
On Fri, Oct 3, 2014 at 6:10 PM, Olivier Grisel
wrote:
> 2014-09-27 4
2014-09-27 4:51 GMT+02:00 Mathieu Blondel :
> This is because LinearSVC doesn't support sample_weight.
>
> I added a new issue for raising a more explicit error message:
> https://github.com/scikit-learn/scikit-learn/issues/3711
>
> BTW, a linear combination of linear models is a linear model itsel
