GLMNET is an R package which implements a fast algorithm for estimation of generalized linear models with convex penalties, such as a linear regression problem. The package isn't able to handle discrete choice multinomial regression.
I want an implementation which is able to handle discrete choice multinomial logistic regression, must be faster than a python numpy implementation and also be straightward to interface/implement with/in python.
Here are important links which will clarify the problem.
Model: [login to view URL]
GLMNET paper: [login to view URL]~hastie/Papers/[login to view URL]
Optimization routine: [login to view URL]
Hi, I am interested in the project. I am familiar with multinomial logistic regression (I used that in microeconometrics), and also I have good skills in numerical optimization. Kind regards.
Hi. thanks for your posting.
I'm very happy to see your project.
I'm a scientist with coding skills.
I've read your project description with deep interest.
I feel that I can help you.
I'm willing to give you what help I can.
looking forward to your kind reply.
best regards.
from zhejing.