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: [url removed, login to view]
GLMNET paper: [url removed, login to view]~hastie/Papers/[url removed, login to view]
Optimization routine: [url removed, login to view]
Pay will be correlated with speed, faster implementations relative to numpy get more money, if the algorithm is slower than numpy + newton method there will be no payment.
The routine needs to be able to handle several million observations with tens of explanatory variables/covariates.
5 freelancers are bidding on average $993 for this job
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.