Thanks for all your helpful replies.
Best,
Francisco
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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
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
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 some
1) AFAIK Spark Python API does not supply interface to set regType and
regParam.
If you want to personalize your own LR model with proper regularized
parameters, strong recommend to user scala API.
You can reference the following code at
spark-1.0.0/python/pyspark/mllib/classification.py.
class Log
