[scikit-learn] meta-estimator for multiple MLPRegressor

[Thomas Evangelidis]

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: tevang at pharm.uoa.gr

          tevang3 at gmail.com


website: https://sites.google.com/site/thomasevangelidishomepage/
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