You can indeed derive from BaseEstimator and implement fit, predict and optionally score.
Here is the documentation for the expected estimator API: http://scikit-learn.org/stable/developers/contributing.html#apis-of-scikit-learn-objects As this is a linear regression model, you can also want to have a look at the LinearModel and RegressionMixin base classes for inspiration: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/base.py#L401 Note that the score function should always be "higher is better". The explained variance ratio and negative mean squared error are valid scoring functions for model selection in scikit-learn while raw MSE is not not. -- Olivier _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
