Even with a single layer with 10 neurons you're still trying to train over 6000 parameters using ~30 samples. Dropout is a concept common in neural networks, but doesn't appear to be in sklearn's implementation of MLPs. Early stopping based on validation performance isn't an "extra" step for reducing overfitting, it's basically a required step for neural networks. It seems like you have a validation sample of ~6 datapoints.. I'm still very skeptical of that giving you proper results for a complex model. Will this larger dataset be of exactly the same data? Just taking another unrelated dataset and showing that a MLP can learn it doesn't mean it will work for your specific data. Can you post the actual results from using LASSO, RandomForestRegressor, GradientBoostingRegressor, and MLP?
On Mon, Jan 9, 2017 at 4:21 PM, Stuart Reynolds <stu...@stuartreynolds.net> wrote: > 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 > >
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