If you have such a small number of observations (with a much higher feature space) then why do you think you can accurately train not just a single MLP, but an ensemble of them without overfitting dramatically?
On Sat, Jan 7, 2017 at 2:26 PM, Thomas Evangelidis <teva...@gmail.com> wrote: > 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 > >
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