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/
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