For ElasticNetCV, inside the function _alpha_grid() it computes the maximum
regularization strength alpha, with a given dataset X, target Y, and L1
ratio, for which there will be at least one nonzero coefficient. I'm
wondering if/how the same could be computed for sklearn's L1/L2-regularized
NMF.
Hi James,
I'm not sure how useful a minimum alpha would be. Even if no weights
are shrunk quite to zero, the regularization can still impact
performance metrics. I would be curious what application you have in
mind for this.
The max alpha question is interesting, I am curious as well. (Sorry my
