[scikit-learn] Efficient forward stepwise regression
Matt Schoenbauer
matt.schoenbauer3 at gmail.com
Thu Apr 22 12:37:26 EDT 2021
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.
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