>>> Afaik, it was with a l1-penalized logistic. In my experience, >>> l2-penalized models and less sensitive to choice of the penality >>> parameter, and hinge loss (aka SVM) and less sensitive than l2 of >>> logistic loss.
indeed. > I think you need a dataset with n_features >> n_samples with many > noisy features, maybe using make_classification with a n_informative > == 0.1 * n_features for instance: exactly I've discovered/suffered from the problem when writing the randomized L1 logistic code where the optimal C using a sample_fraction < 1 was leading to a bad C for the full problem. Alex ------------------------------------------------------------------------------ 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
