Hello sklearn developers, I'd like to implement a forward stepwise regression algorithm using the efficient procedure described in the first problem here <http://stat.rutgers.edu/home/hxiao/stat588_2011/hw1.pdf>. It does not seem that such a model exists anywhere in Python. Would it be useful for me to write this model up for sklearn?
If you're interested, here's a high-level view of how I think it would work: - The model would have sklearn.linear_model.LinearRegression as its base class. - The additional model parameters would include - An array of the indices (or column names) of the features in X1 - The Q and R matrices - The additional methods would include - An add_features() method that adds a specified number of features to the model. Updates all model parameters - A fit() method that requires a specification of the number of parameters to fit and optional sample weight. It calls the add_features method once on a model with no features. I would do this for OLS first, but supposedly it could be adapted for regularized models as well. How does this sound? Thanks, Matt S.
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
