Hi Alex, When I mentionned that to James, he seem to imply that this approach was useful only to optimize many parameters, around 8 or more. You would have to confirm this. I believe that he'll be around at the sprints. I far as I am concerned, I don't optimize that number of parameters in the scikit.
Gaël On Mon, Nov 14, 2011 at 10:06:36PM -0500, Alexandre Passos wrote: > Recent work by James Bergstra demonstrated that careful hyperparameter > optimization, as well as careless random sampling, is often better > than manual searching for many problems. You can see results in the > following nips paper: > http://people.fas.harvard.edu/~bergstra/files/pub/11_nips_hyperopt.pdf > I wonder if there's interest in adding some simple versions of these > techniques to the scikit's very useful GridSearchCV? There is code > available https://github.com/jaberg/hyperopt but it seems to be > research code and it uses theano, so it's not applicable to the > scikit. ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
