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https://issues.apache.org/jira/browse/SPARK-17163?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15440142#comment-15440142
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Joseph K. Bradley commented on SPARK-17163:
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I was guessing that optimization would be more likely to diverge and return
blown-up coefficients when not pivoting with regParam=0 (more likely than when
pivoting). A given training dataset could constrain the problem enough to make
a well-defined optimal solution with regParam=0 and pivoting, but the same
might not hold true when not pivoting.
> Merge MLOR into a single LOR interface
> --------------------------------------
>
> Key: SPARK-17163
> URL: https://issues.apache.org/jira/browse/SPARK-17163
> Project: Spark
> Issue Type: Sub-task
> Components: ML, MLlib
> Reporter: Seth Hendrickson
>
> Before the 2.1 release, we should finalize the API for logistic regression.
> After SPARK-7159, we have both LogisticRegression and
> MultinomialLogisticRegression models. This may be confusing to users and, is
> a bit superfluous since MLOR can do basically all of what BLOR does. We
> should decide if it needs to be changed and implement those changes before 2.1
> *Update*: Seems we have decided to merge the two estimators. I changed the
> title to reflect that.
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