[scikit-learn] Efficient forward stepwise regression
alexandre.gramfort at inria.fr
Mon Apr 26 02:23:03 EDT 2021
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?
to be considered I would first ask you to evaluate and discuss what you
think it will bring
over existing estimators. Typically do you foresee a clear benefit compared
to Lars or LassoLars ?
For more see
> If you're interested, here's a high-level view of how I think it would
> - The model would have sklearn.linear_model.LinearRegression as its base
> - 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.
the API of scikit-learn estimator is quite strict. See
I invite you to read
if you are willing to help the team.
> I would do this for OLS first, but supposedly it could be adapted for
> regularized models as well.
> How does this sound?
> Matt S.
> scikit-learn mailing list
> scikit-learn at python.org
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