Github user dbtsai commented on the pull request: https://github.com/apache/spark/pull/1379#issuecomment-63904113 @avulanov I will merge this on Spark 1.3, and sorry for delay since I was very busy recently. Yes, the branch you found should work, but it can not be cleanly merged in upstream, and I'm working on it. You can try that branch for now. Also, in the branch, we don't use LBFGS as optimizer, so the convergent rate will be slow. Basically, you can model the whole problem using (num_features + 1)(num_classes), but the solution will not be unique. You can chose one of the class as base class to make the solution unique, and I chose the first class as base class. See `Properties of softmax regression parameterization` in the wiki page you refer. Or my presentation http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297 for more technical detail. You can think about binary logistic regression, and you only have (num_features + 1) coefficients instead of 2 * (num_features + 1)
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