Github user dbtsai commented on the issue:
https://github.com/apache/spark/pull/14834
@sethah I remember that `compressed` method for `Matrix` is one of the todo
in the followup tasks. For sparse binary logistic regression, if we store the
models as `1 x numFeatures` compressed sparse row major matrices, I think the
space will be the same as current sparse vector implementation. And this CSR
format should be able to convert to sparse vector without changing the
underline data structure.
Throwing an exception in the case of 2 classes with multinomial family
should good to me.
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