Makes sense!
On Wed, Mar 25, 2015 at 2:46 PM, Debasish Das
wrote:
> Cool...Thanks...It will be great if they move in two code paths just for
> the sake of code clean-up
>
> On Wed, Mar 25, 2015 at 2:37 PM, DB Tsai wrote:
>
>> I did the benchmark when I used the if-else statement to switch the
>
Cool...Thanks...It will be great if they move in two code paths just for
the sake of code clean-up
On Wed, Mar 25, 2015 at 2:37 PM, DB Tsai wrote:
> I did the benchmark when I used the if-else statement to switch the
> binary & multinomial logistic loss and gradient, and there is no
> performanc
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
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
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
