A fast multinomial logistic optimization routine

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$750-1500 USD

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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]

ID del proyecto: 5095772

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Activo hace 11 años

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4 freelancers están ofertando un promedio de $866 USD por este trabajo

@OriolAM

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.

$777 USD en 3 días

4,6

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5,6

@zhejing1028

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.

$777 USD en 9 días