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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