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
