It is written in the documentation of the loss parameter:
string, 'l1' or 'l2' (default='l2')
Specifies the loss function. 'l1' is the hinge loss (standard SVM)
while 'l2' is the squared hinge loss.
but clearly it can be passed unseen with a quick reading.
2012/8/21 Andreas Mueller <[email protected]>
> Hi Alexis.
> That must be it! Crazy.
> We should definitely point this out in the docs somewhere.
> Not sure how I overlooked that.
> Thanks a lot!
>
> Andy
>
>
> On 08/21/2012 10:52 AM, Alexis Mignon wrote:
>
> Another big difference is that the default loss function in Linear SVC is
> quadractic with distance to the margin (loss='l2') while it is linear in
> SVC.
>
> 2012/8/12 Mathieu Blondel <[email protected]>
>
>>
>>
>> On Sun, Aug 12, 2012 at 6:53 PM, Andreas Mueller <
>> [email protected]> wrote:
>>
>>> Does any one have an explanation for that?
>>> Btw, I am using the sparse versions to do some text classification.
>>>
>>
>> One difference is that SVC fits the intercept directly (without using the
>> dummy feature trick). So the intercept is not penalized. Howerver, the
>> intercept shouldn't change the results much with high dimensional data.
>>
>> Mathieu
>>
>>
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