Github user dbtsai commented on the issue:
https://github.com/apache/spark/pull/14834
@sethah +1 for this approach. Couple minor questions. With L1, the
coefficients can be very sparse. Currently, we will store them as sparse vector
and use sparse vector for prediction. (It is decided to store as sparse or
dense vector based on size, not as prediction speed, and we probably need to do
some experiment around it). Do you plan to always store the coefficients as
dense matrix even for binomial case?
Also, for 2 classes LOR with multinomial family, will users be able to `def
coefficients: Vector` and `def intercept: Double` by pivoting the coefficients?
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