Thanks Phuong But the point of my post is how to achieve without using the deprecated the mllib pacakge. The mllib package already has multinomial regression built in
2016-05-28 21:19 GMT-07:00 Phuong LE-HONG <phuon...@gmail.com>: > Dear Stephen, > > Yes, you're right, LogisticGradient is in the mllib package, not ml > package. I just want to say that we can build a multinomial logistic > regression model from the current version of Spark. > > Regards, > > Phuong > > > > On Sun, May 29, 2016 at 12:04 AM, Stephen Boesch <java...@gmail.com> > wrote: > > Hi Phuong, > > The LogisticGradient exists in the mllib but not ml package. The > > LogisticRegression chooses either the breeze LBFGS - if L2 only (not > elastic > > net) and no regularization or the Orthant Wise Quasi Newton (OWLQN) > > otherwise: it does not appear to choose GD in either scenario. > > > > If I have misunderstood your response please do clarify. > > > > thanks stephenb > > > > 2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <phuon...@gmail.com>: > >> > >> Dear Stephen, > >> > >> The Logistic Regression currently supports only binary regression. > >> However, the LogisticGradient does support computing gradient and loss > >> for a multinomial logistic regression. That is, you can train a > >> multinomial logistic regression model with LogisticGradient and a > >> class to solve optimization like LBFGS to get a weight vector of the > >> size (numClassrd-1)*numFeatures. > >> > >> > >> Phuong > >> > >> > >> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <java...@gmail.com> > >> wrote: > >> > Followup: just encountered the "OneVsRest" classifier in > >> > ml.classsification: I will look into using it with the binary > >> > LogisticRegression as the provided classifier. > >> > > >> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <java...@gmail.com>: > >> >> > >> >> > >> >> Presently only the mllib version has the one-vs-all approach for > >> >> multinomial support. The ml version with ElasticNet support only > >> >> allows > >> >> binary regression. > >> >> > >> >> With feature parity of ml vs mllib having been stated as an objective > >> >> for > >> >> 2.0.0 - is there a projected availability of the multinomial > >> >> regression in > >> >> the ml package? > >> >> > >> >> > >> >> > >> >> > >> >> ` > >> > > >> > > > > > >
