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.
