You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/07/29 11:52:20 UTC
[jira] [Resolved] (SPARK-16768) pyspark calls incorrect version of
logistic regression
[ https://issues.apache.org/jira/browse/SPARK-16768?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-16768.
-------------------------------
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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org