[jira] [Updated] (SPARK-17718) Make loss function formulation label note clearer in MLlib docs

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17718?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-17718:
------------------------------
    Summary: Make loss function formulation label note clearer in MLlib docs  (was: Update MLib Classification Documentation )

> 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
>            Priority: Minor
>
> 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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