You can combine your own learning algorithm, bet it a neural net or anything else, by simply adhering to the scikit-learn interface as shown below. Then register your class with the Register module and you're done!
from sklearn.base import BaseEstimator class CustomEstimator(BaseEstimator): def __init__(self, param1=0, param2=None): # it is important that you name your params the same in the constructor # stub as well as in your class variables! self.param1 = param1 self.param2 = param2 def fit(self, data, targets=None, **kwargs): """ Adjust the underlying model or method to the data. Returns ------- IMPORTANT: must return self! """ return self def predict(self, data): """ Use the learned model to make predictions. """ my_predictions =  return my_predictions