On Wed, Mar 30, 2011 at 11:44 PM, Yaroslav Halchenko <[email protected]> wrote: > > On Wed, 30 Mar 2011, Per B. Sederberg wrote: >> clfr = GLMNET_R(alpha=.5, model_type='naive', enable_ca=['estimates']) > > so it is a regression. Wrap with RegressionAsClassifier to gain > classification ;) > > thus metrics for regressions: > >> -------> print(sclf.ca.stats) >> Statistics Mean Std Min Max >> ---------- ----- ----- ----- ----- >> Data: >> RMP_t 2.285 0.696 1.3 4 >> STD_t 0 0 0 0 >> >...< >
I actually want the regression and not the classifier, but I now totally understand the stats printout. I was confused because the CCe is 1 minus the correlation. >> Also, I really just want to get the predictions/estimates of the >> classifier from each fold, but I don't know how to get that out. > > those, since you have stats, are also available within > sclf.ca.stats.sets > Awesome! Yes, I had just found that and the exact keys I needed were: cv.ca.stats.stats['RMP_p_all'] cv.ca.stats.stats['RMP_t_all'] Now I think I'm all set! Thanks, P > -- > =------------------------------------------------------------------= > Keep in touch www.onerussian.com > Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa > _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
