Well, as you said, MLLib already supports GLM in a sense. Except they only support two link functions - identity (linear regression) and logit (logistic regression). It should not be too hard to add other link functions, as all you have to do is add a different gradient function for Poisson/Gamma, etc - look at Gradient.scala in mllib.
On Tue, Jun 17, 2014 at 5:00 PM, Xiaokai Wei <x...@palantir.com> wrote: > Hi, > > I am an intern at PalantirTech and we are building some stuff on top of > MLlib. In Particular, GLM is of great interest to us. Though > GeneralizedLinearModel in MLlib 1.0.0 has some important GLMs such as > Logistic Regression, Linear Regression, some other important GLMs like > Poisson Regression are still missing. > > I am curious that if anyone is already working on other GLMs (e.g. > Poisson, Gamma). If not, we would like to contribute to MLlib on GLM. Is > adding more GLMs on the roadmap of MLlib? > > > Sincerely, > > Xiaokai >
