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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 04:31:23 GMT
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 <phuonglh@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 <javadba@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 <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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