Github user sethah commented on the issue:
https://github.com/apache/spark/pull/14834
@dbtsai Good point. This patch in its current state would change the
behavior of binomial LOR to always have dense coefficients. I think we need to
find a solution to this. I wonder why there isn't a `compressed` method for
`Matrix`?
If we store the coefficients as `SparseMatrix` in some L1 cases, then
before prediction we have to convert it to a `SparseVector`. This amounts to an
extra `4 * nnz` bytes being stored (we have to create the sparse vector indices
since we cannot reuse them from the matrix case). We could implement a
`compressed` method for matrices if we are ok with the extra storage overhead.
Otherwise I guess we'd have to store the binomial case as a vector and then
do some conversion to matrix iff `coefficientMatrix` is called.
Finally, I don't think it's necessary to pivot the coefficients in the case
of 2 classes with multinomial family. Currently, we throw an exception.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
