On 04/01/2012 09:27 PM, Alexandre Gramfort wrote: >>>> 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. > I created an issue to discuss this further. I'd really like to have a good solution, as I really like to address this in the release.
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