On 08/28/2016 12:29 PM, Raphael C wrote:
To give a little context from the web, see e.g.
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/ where
it explains:
"
A question might have come to your mind by now: if we find two
matrices \mathbf{P} and \mathbf{Q} such that \mathbf{P} \times
\mathbf{Q} approximates \mathbf{R}, isn’t that our predictions of all
the unseen ratings will all be zeros? In fact, we are not really
trying to come up with \mathbf{P} and \mathbf{Q} such that we can
reproduce \mathbf{R} exactly. Instead, we will only try to minimise
the errors of the observed user-item pairs.
"
Yes, the sklearn interface is not meant for matrix completion but
matrix-factorization.
There was a PR for some matrix completion for missing value imputation
at some point.
In general, scikit-learn doesn't really implement anything for
recommendation algorithms as that requires a different interface.
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
