If you dont have a large dataset, you can still do leave one out cross validation.
On Mon, Jan 9, 2017 at 3:42 PM Thomas Evangelidis <teva...@gmail.com> wrote: > > Jacob & Sebastian, > > I think the best way to find out if my modeling approach works is to find > a larger dataset, split it into two parts, the first one will be used as > training/cross-validation set and the second as a test set, like in a real > case scenario. > > Regarding the MLPRegressor regularization, below is my optimum setup: > > MLPRegressor(random_state=random_state, max_iter=400, early_stopping=True, > validation_fraction=0.2, alpha=10, hidden_layer_sizes=(10,)) > > > This means only one hidden layer with maximum 10 neurons, alpha=10 for L2 > regularization and early stopping to terminate training if validation score > is not improving. I think this is a quite simple model. My final predictor > is an SVR that combines 2 MLPRegressors, each one trained with different > types of input data. > > @Sebastian > You have mentioned dropout again but I could not find it in the docs: > > http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor > > Maybe you are referring to another MLPRegressor implementation? I have > seen a while ago another implementation you had on github. Can you clarify > which one you recommend and why? > > > Thank you both of you for your hints! > > best > Thomas > > > > -- > > > > > > > > > > > > > > > > > ====================================================================== > > > Thomas Evangelidis > > > Research Specialist > CEITEC - Central European Institute of Technology > Masaryk University > Kamenice 5/A35/1S081, > 62500 Brno, Czech Republic > > email: tev...@pharm.uoa.gr > > > teva...@gmail.com > > > > website: > > https://sites.google.com/site/thomasevangelidishomepage/ > > > > > > > > > > _______________________________________________ > > 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