On Sun, Mar 18, 2012 at 4:45 AM, James Bergstra <[email protected]> wrote:
> I agree that it's a good idea to correct C for sample size when moving > from a sub-problem to the full thing. I just wouldn't use the word > "optimal" to describe the new value of C that you get this way - it's > an extrapolation, a good guess... possibly provably better than the > un-corrected value of C, but I would balk at claiming that it's > optimal. Completely agreed. The assumption that the relation between the training set size and the optimal C value is linear may not hold in some cases (for example, if the data lies in highly dense regions and sparse regions). I also agree that the only safe way to tune C is to use held-out data: this way we don't need to refit the best model. > The alpha specified this way could (should?) have the same name and > interpretation as the l2_regularization coefficient in the > SGDClassifier. Would you convert alpha into a C internal value or would you patch libsvm / liblinear to use alpha? I don't understand how the former would be different from the scale_C option, in practice. Mathieu ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
