1) This is a miss, unfortunately ... We will add support for regularization and intercept in the coming v1.1. (JIRA: https://issues.apache.org/jira/browse/SPARK-2550) 2) It has overflow problems in Python but not in Scala. We can stabilize the computation by ensuring exp only takes a negative value: 1 / ( 1 + e^ x) = 1 - 1 / ( 1 + e^{-x} ) . (JIRA: https://issues.apache.org/jira/browse/SPARK-2552)
-Xiangrui On Wed, Jul 16, 2014 at 7:58 PM, Yanbo Liang <yanboha...@gmail.com> wrote: > AFAIK for question 2, there is no built-in method to account for that > problem. > At right now, we can only perform one type of regularization. > However, the elastic net implementation is just underway. > You can refer this topic for further discussion. > https://issues.apache.org/jira/browse/SPARK-1543 > > > 2014-07-17 2:08 GMT+08:00 fjeg <francisco.gime...@gmail.com>: > >> 1) Okay, to clarify, there is *no* way to regularize logistic regression >> in >> python (sorry if I'm repeating your answer). >> >> 2) This method you described will have overflow errors when abs(margin) > >> 750. Is there a built-in method to account for this? Otherwise, I will >> probably have to implement something like this: >> >> http://lingpipe-blog.com/2012/02/16/howprevent-overflow-underflow-logistic-regression >> >> Also, another question about the Scala implementation: >> Can we only do one type of regularization? Is there any way to perform >> elastic net which is a combination of L1 and L2? >> >> >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/MLLib-Regularized-logistic-regression-in-python-tp9780p9963.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. > >
