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From DB Tsai <>
Subject Re: LogisticGradient Design
Date Wed, 25 Mar 2015 21:37:54 GMT
I did the benchmark when I used the if-else statement to switch the
binary & multinomial logistic loss and gradient, and there is no
performance hit at all. However, I'm refactoring the LogisticGradient
code so the addBias and scaling can be done in LogisticGradient
instead of the input dataset to avoid the second cache. In this case,
the code will be more complicated, so I will split the code into two
paths. Will be done in another PR.


DB Tsai

On Wed, Mar 25, 2015 at 11:57 AM, Joseph Bradley <> wrote:
> 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).
> Joseph
> On Wed, Mar 25, 2015 at 8:15 AM, Debasish Das <>
> wrote:
>> 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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