hi Matt, 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 https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms > 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. > > the API of scikit-learn estimator is quite strict. See https://scikit-learn.org/stable/developers/develop.html?highlight=check_estimator I invite you to read https://scikit-learn.org/stable/developers/contributing.html?highlight=contribut if you are willing to help the team. Alex > 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 >
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