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From Joseph Bradley <>
Subject Re: LogisticGradient Design
Date Wed, 25 Mar 2015 18:57:15 GMT
It would be nice to see how big a performance hit we take from combining
binary & multiclass logistic loss/gradient.  If it's not a big hit, then it
might be simpler from an outside API perspective to keep them in 1 class
(even if it's more complicated within).

On Wed, Mar 25, 2015 at 8:15 AM, Debasish Das <>

> Hi,
> Right now LogisticGradient implements both binary and multi-class in the
> same class using an if-else statement which is a bit convoluted.
> For Generalized matrix factorization, if the data has distinct ratings I
> want to use LeastSquareGradient (regression has given best results to date)
> but if the data has binary labels 0/1 based on domain knowledge (implicit
> for example, visits no-visits) I want to use a LogisticGradient without any
> overhead for multi-class if-else...
> I can compare the performance of LeastSquareGradient and multi-class
> LogisticGradient on the recommendation metrics but it will be great if we
> can separate binary and multi-class in Separate
> classes....MultiClassLogistic can extend BinaryLogistic but mixing them in
> the same class is an overhead for users (like me) who wants to use
> BinaryLogistic for his application..
> Thanks.
> Deb

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