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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