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? >> >> >> >> >> >> >> >> >> >> ` >> > >> > > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
