it could also be that folds are different and that LIBSVM does maybe
some per fold
scaling. I would look at the scores on each grid point to see if the
output of libsvm
gives a similar score.

Alex

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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