[scikit-learn] meta-estimator for multiple MLPRegressor

[Stuart Reynolds]

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 <tevang3 at 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
>
>
>
> --
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> ======================================================================
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>
> 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
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> website:
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> https://sites.google.com/site/thomasevangelidishomepage/
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