Regarding the evaluation, I use the leave 20% out cross validation method. I cannot leave more out because my data sets are very small, between 30 and 40 observations, each one with 600 features. Is there a limit in the number of MLPRegressors I can combine with stacking considering my small data sets?
On Jan 7, 2017 23:04, "Joel Nothman" <joel.noth...@gmail.com> wrote: > * > > >> There is no problem, in general, with overfitting, as long as your >> evaluation of an estimator's performance isn't biased towards the training >> set. We've not talked about evaluation. >> > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
