On 03/22/2012 02:11 AM, Olivier Grisel wrote: > Le 22 mars 2012 01:09, David Warde-Farley<[email protected]> a écrit > : > >> >>> That said, I agree with James that the docs should be much more >>> explicit about what is going on, and how what we have differs from >>> libsvm. >>> >> I think that renaming sklearn's scaled version of "C" is probably a start. >> Using the name "C" for something other than what everyone else means by "C" >> violates the principle if least surprise quite severely. If they saw "zeta" >> or "Francis" or "unicorn", most people will not assume it is a moniker for C >> but refer to the documentation for an explanation. >> > +1 for not using the parameter name "C" if it's not the same "C" as in > the SVM literature. > > Something that bothers me though, is that with libsvm, C=1 or C=10 > seems to be a reasonable default that work well both for dataset with > size n_samples=100 and n_samples=10000 (by playing with the range of > datasets available in the scikit). On the other hand alpha would have > to be grid searched systematically: > > It is also my gut feeling that dividing the regularization term by > n_samples make the optimal value *more* dependent on the dataset size > rather that the opposite. That might be the reason why C is not scaled > in the SVM literature. Off course I might be wrong as I have not done > any kind of systematic cross-datasets analysis. > > I had the same feeling and I think we should really investigate this. Volunteers? ;)
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