Github user goodsoldiersvejk commented on the issue:
https://github.com/apache/spark/pull/13516
Thanks for your comment. The exact wording can be made more explicit, but
that key point is implicit in the conditional distribution of y given x being
modeled in logistic regression. The Spark mllib documentation attempts to and I
think should provide a balance between operational use and context. The Spark
user wonders why choose logistic regression over linear svms if operationally
they can be the same but "the raw output of the logistic regression .... has a
probabilistic interpretation". This hints at but skirts the difference in
methodology by which the probabilistic interpretation becomes obvious. I would
still rewrite this to provide context for Bayesian methods (maybe say a
reference link).
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