[scikit-learn] Question-Early Stopping MLPClassifer RandomizedSearchCV

[Andreas Mueller]

Yes, you understood correctly.
You can see the implementation in the code:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neural_network/multilayer_perceptron.py#L491

It calls ``train_test_split``, so it's a random subset of the data. 
Currently the API doesn't allow providing your own validation set.
What is the use-case for that?

Andy

On 08/11/2017 05:57 PM, fabian.sippl at gmx.net wrote:
> Hello Scikit-Learn Team,
> I´ve got a question concerning the implementation of Early Stopping in 
> MLPClassifier. I am using it in combination with RandomizedSearchCV. 
> The fraction used for validation in early stopping is set with the 
> parameter validation_fraction of MLPClassifier. How is the validaton 
> set extracted from the training set ? Does the function simply take 
> the last X % from the training set ? Is there a possibility to 
> manually set this validation set ?
> I wonder whether I correctly understand the functionality: The neural 
> net is trained on the training data and the performance is evaluated 
> after every epoch on the validation set (which is internally selected 
> by the MLPClassifer)? If the Net stops training, the performance on 
> the left out data (Parameter "cv" in RandomizedSearch) is determined ?
> Thank you very much for your help !
> Kind Regards,
> Fabian Sippl
>
>
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