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https://issues.apache.org/jira/browse/SPARK-16768?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-16768.
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Resolution: Not A Problem
Fix Version/s: (was: 2.1.0)
You're importing the .mllib version. I'm saying there is no
ml.LogisticRegressionWithLBFGS. Note mllib vs ml. But that's different from
your original question. I don't see what you're referring to in the doc, and am
not sure what you're saying isn't reliable. L-BFGS remains implemented in both
APIs.
> pyspark calls incorrect version of logistic regression
> ------------------------------------------------------
>
> Key: SPARK-16768
> URL: https://issues.apache.org/jira/browse/SPARK-16768
> Project: Spark
> Issue Type: Bug
> Components: MLlib, PySpark
> Environment: Linux openSUSE Leap 42.1 Gnome
> Reporter: Colin Beckingham
>
> PySpark call with Spark 1.6.2 "LogisticRegressionWithLBFGS.train()" runs
> "treeAggregate at LBFGS.scala:218" but the same command in pyspark with Spark
> 2.1 runs "treeAggregate at LogisticRegression.scala:1092". This non-optimized
> version is much slower and produces a different answer from LBFGS.
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