Hi Jose.
Yes, that's the parameter I mean.
I think it might be lower by default in libsvm.
You can check by looking at their help. I don't have it installed on my
box atm so I can't check.

Otherwise, just set it to 1e-6 and see if that makes a difference.
If not, you could investigate the cross validation folds as
suggested by Alex.

Cheers,
Andy


On 08/20/2012 09:19 AM, José Guilherme Camargo de Souza wrote:
>     Hi,
>
>     I'm using sklearn 0.11
>
> In [3]: import sklearn
>
> In [4]: print sklearn.__version__
> 0.11
>
>     By default tolerance you mean the 'tol' parameter?
>
> tol: float, optional (default=1e-3) :
> Tolerance for stopping criterion.
>
>     Thanks,
>
>
> José Guilherme
>
>
> On Tue, Aug 14, 2012 at 2:18 PM, Andreas Müller
> <[email protected]> wrote:
>> Hi Jose.
>> We are using the LIBSVM library as backend, so in principle the results
>> should be identical. It could be that the default tolerance (tol) is 
>> different.
>>
>> Which version of sklearn are you using?
>>
>> Cheers,
>> Andy
>>
>> ----- Ursprüngliche Mail -----
>> Von: "José Guilherme Camargo de Souza" <[email protected]>
>> An: [email protected]
>> Gesendet: Dienstag, 14. August 2012 13:16:19
>> Betreff: [Scikit-learn-general] Differences between pure LIBSVM and     
>> Scikit-learn SVR implementation
>>
>>     Hi all,
>>
>>     I'm new to scikit-learn and I'm trying to reproduce some results
>> obtained with LIBSVM. I'm using the SVM regression and doing grid
>> search to find the optimal C, gamma and epsilon for my training
>> dataset.
>>
>>     The ranges for the search using libsvm implementation are:
>>
>>     C = [1, 10]
>>     gamma = [0.0001, 0.01]
>>     epsilon= [0.1, 0.2]
>>
>>     When I try the same values with the grid search + SVR implemented
>> in Scikit-learn I get different results. I wonder how should I adapt
>> the values above to the scikit-learn implementation. Does anyone have
>> some pointers?
>>
>>     Thanks,
>>
>>
>> José Guilherme
>>
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