Given that we'd love to get rid of our libsvm/liblinear biddings, I would
be more in favor of improving our matrix factorization code rather than
including this code.
That said, +1 for missing data imputation with matrix factorization, once
we're done with the current PRs on missing data.
Gaël
O
(I am not a scikit learn dev.)
This is a great idea and I for one look forward to using it.
My understanding is that libmf optimises only over the observed values
(that is the explicitly given values in a sparse matrix) as is typically
needed for recommender system whereas the scikit learn NMF co
Forwarded Message
Subject:libmf bindings
Date: Wed, 2 Nov 2016 11:38:00 -0400
From: sam royston
To: scikit-learn-ow...@python.org
Hi,
Thanks for all your hard work on this useful tool! I'm hoping to
contribute bindings to Chih-Jen Lin's libmf:
https://ww
