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From Stephen Boesch <java...@gmail.com>
Subject Re: Multinomial regression with spark.ml version of LogisticRegression
Date Sun, 29 May 2016 04:04:45 GMT
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 <phuonglh@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 <javadba@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 <javadba@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?
> >>
> >>
> >>
> >>
> >> `
> >
> >
>

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