Custom Estimator

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.

        IMPORTANT: must return self!
        return self

    def predict(self, data):
        Use the learned model to make predictions.
        my_predictions = []
        return my_predictions