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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17718) Make loss function formulation label note clearer in MLlib docs
Date Mon, 03 Oct 2016 09:45:20 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17718?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15542004#comment-15542004
] 

Apache Spark commented on SPARK-17718:
--------------------------------------

User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/15330

> Make loss function formulation label note clearer in MLlib docs
> ---------------------------------------------------------------
>
>                 Key: SPARK-17718
>                 URL: https://issues.apache.org/jira/browse/SPARK-17718
>             Project: Spark
>          Issue Type: Documentation
>            Reporter: Tobi Bosede
>            Assignee: Sean Owen
>            Priority: Trivial
>
> https://spark.apache.org/docs/1.6.0/mllib-linear-methods.html#mjx-eqn-eqregPrimal
> The loss function here for logistic regression is confusing. It seems to imply that spark
uses only -1 and 1 class labels. However it uses 0,1.  Note below needs to make this point
more visible to avoid confusion.
> "Note that, in the mathematical formulation in this guide, a binary label
> y is denoted as either +1 (positive) or −1 (negative), which is convenient
> for the formulation. However, the negative label is represented by 0 in
> spark.mllib instead of −1, to be consistent with multiclass labeling."
> Better yet, the loss function should be replaced with that for 0, 1 despite mathematical
inconvenience, since that is what is actually implemented. 



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