Olivier, actually, SGDRegressor is best on boston (of those that give coefs) so that would be my first choice, for problems big or small.
Grid search ? who has the time ? OK ... in fact L1-regularization shrinks coefs and R2 towards 0 but av, max |residuals| get worse -- SGDRegressor boston penalty=l1 for alpha in .01 1 2 4 R2 .717 |res| av 2.65 max 15.5 coef [-.2 -1 .5 -1 1 -.7 2.4 ... R2 .681 |res| av 2.84 max 17.1 coef [-.2 -.7 0 -.2 .1 0 2.1 ... R2 .628 |res| av 2.95 max 18.8 coef [-.2 -.4 0 0 0 0 1.7 ... R2 .464 |res| av 3.34 max 22.7 coef [-.3 0 0 0 0 0 .8 ... Toy 2D yet informative datasets ? Maybe fun but I think a chimera, and 2D is not nD. A couple more *real* datasets should be trivial ? cheers -- denis ------------------------------------------------------------------------------ Monitor your physical, virtual and cloud infrastructure from a single web console. Get in-depth insight into apps, servers, databases, vmware, SAP, cloud infrastructure, etc. Download 30-day Free Trial. Pricing starts from $795 for 25 servers or applications! http://p.sf.net/sfu/zoho_dev2dev_nov _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
