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From Bharath Ravi Kumar <reachb...@gmail.com>
Subject Re: LogisticRegression: Predicting continuous outcomes
Date Thu, 29 May 2014 10:11:44 GMT
Xiangrui, Christopher,

Thanks for responding.  I'll  go through the code in detail to evaluate if
the loss function used is suitable to our dataset. I'll also go through the
referred paper since I was unaware of the underlying theory. Thanks again.

-Bharath


On Thu, May 29, 2014 at 8:16 AM, Christopher Nguyen <ctn@adatao.com> wrote:

> Bharath, (apologies if you're already familiar with the theory): the
> proposed approach may or may not be appropriate depending on the overall
> transfer function in your data. In general, a single logistic regressor
> cannot approximate arbitrary non-linear functions (of linear combinations
> of the inputs). You can review works by, e.g., Hornik and Cybenko in the
> late 80's to see if you need something more, such as a simple, one
> hidden-layer neural network.
>
> This is a good summary:
>
> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.101.2647&rep=rep1&type=pdf
>
> --
> Christopher T. Nguyen
> Co-founder & CEO, Adatao <http://adatao.com>
> linkedin.com/in/ctnguyen
>
>
>
> On Wed, May 28, 2014 at 11:18 AM, Bharath Ravi Kumar <reachbach@gmail.com
> >wrote:
>
> > I'm looking to reuse the LogisticRegression model (with SGD) to predict a
> > real-valued outcome variable. (I understand that logistic regression is
> > generally applied to predict binary outcome, but for various reasons,
> this
> > model suits our needs better than LinearRegression). Related to that I
> have
> > the following questions:
> >
> > 1) Can the current LogisticRegression model be used as is to train based
> on
> > binary input (i.e. explanatory) features, or is there an assumption that
> > the explanatory features must be continuous?
> >
> > 2) I intend to reuse the current class to train a model on LabeledPoints
> > where the label is a real value (and not 0 / 1). I'd like to know if
> > invoking setValidateData(false) would suffice or if one must override the
> > validator to achieve this.
> >
> > 3) I recall seeing an experimental method on the class (
> >
> >
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
> > )
> > that clears the threshold separating positive & negative predictions.
> Once
> > the model is trained on real valued labels, would clearing this flag
> > suffice to predict an outcome that is continous in nature?
> >
> > Thanks,
> > Bharath
> >
> > P.S: I'm writing to dev@ and not user@ assuming that lib changes might
> be
> > necessary. Apologies if the mailing list is incorrect.
> >
>

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