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.
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