This is currently being worked on, planned for 2.1 I believe https://issues.apache.org/jira/browse/SPARK-7159 On May 28, 2016 9:31 PM, "Stephen Boesch" <java...@gmail.com> wrote:
> 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? >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> ` >> >> > >> >> > >> > >> > >> > >
