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 <debasish.da...@gmail.com> 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 >
