> Hi Alexandre, I haven't been checking my email and I heard about your > message last night from a slightly drunken Gramfort, Grisel, Pinto and > Poilvert in French in a loud bar here in Cambridge. Thanks for the PR > :)
too much information :) > I think there are some findings on this topic that would be good and > appropriate for scikits, and easy to do. > > 1. random sampling should generally be used instead of grid search. > They may feel similar, but theoretically and empirically, sampling > from a hypercube parameter space will typically work better than > iterating over the points of a grid lattice for hyper-parameter > optimization. For some response functions the lattice can be slightly > more efficient, but risks being terribly inefficient. So if you have > to pick one, pick uniform sampling. > > 2. Gaussian process w. Expected Improvement global optimization. > This is an established technique for global optimization that has > about the right scaling properties to be good for hyper-parameter > optimization. I think you probably can't do much better than a > Gaussian Process (GP) with Expected Improvement (EI) for optimizing > the parameters of say, an SVM, but we can only try and see (and > compare with the variety of other techniques for global optimization). > The scikit already has GP fitting in it, scipy has good optimization > routines, so why not put them together to make a hyper-parameter > optimizer? I think this would be a good addition to the scikit, and > not too hard (the hard parts are already done). can you point us to some pdfs ? or maybe write some kind of pseudo code? And as usual pull request / patch welcome :) Alex ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
