I finally found a desk and some focus. I addressed Mathieu's suggestions and added some timings on real data (with a lot of concessions so that it would run reasonably quick on my machine).
Here's the results: http://nbviewer.ipython.org/7224672 It becomes clear that `tol` still means different things between the two solvers. I think the convergence plots are interesting, not only confirming that the solvers work well but it seems that for tall sparse data, projected gradient is better. The non-convergence in the first scenario seems data-dependent (it didn't happen yesterday). L1 regularization seems all the more helpful on real data, I would have expected the slowest two curves to be the other way around though. Active set becomes unusably slow, which explains the issue re: slow performance in transform. Cheers, Vlad On Fri, Nov 8, 2013 at 12:48 PM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > On Fri, Nov 08, 2013 at 11:56:24AM +0100, Olivier Grisel wrote: >> In retrospect I would have prefered it named something explicit like >> "regularization" or "l2_reg" instead of "alpha". > > Agreed. > >> Still I like the (alpha, l1_ratio) parameterization better over the >> (l2_reg, l1_reg) parameter set > > Absolutely. > > G > > ------------------------------------------------------------------------------ > November Webinars for C, C++, Fortran Developers > Accelerate application performance with scalable programming models. Explore > techniques for threading, error checking, porting, and tuning. Get the most > from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Shape the Mobile Experience: Free Subscription Software experts and developers: Be at the forefront of tech innovation. Intel(R) Software Adrenaline delivers strategic insight and game-changing conversations that shape the rapidly evolving mobile landscape. Sign up now. http://pubads.g.doubleclick.net/gampad/clk?id=63431311&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
