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https://issues.apache.org/jira/browse/SPARK-21306?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yanbo Liang updated SPARK-21306:
--------------------------------
    Fix Version/s:     (was: 2.1.2)
                       (was: 2.0.3)

> OneVsRest Conceals Columns That May Be Relevant To Underlying Classifier
> ------------------------------------------------------------------------
>
>                 Key: SPARK-21306
>                 URL: https://issues.apache.org/jira/browse/SPARK-21306
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.1.1
>            Reporter: Cathal Garvey
>            Assignee: Yan Facai (颜发才)
>            Priority: Critical
>              Labels: classification, ml
>             Fix For: 2.2.1, 2.3.0
>
>
> Hi folks, thanks for Spark! :)
> I've been learning to use `ml` and `mllib`, and I've encountered a block 
> while trying to use `ml.classification.OneVsRest` with 
> `ml.classification.LogisticRegression`. Basically, [here in the 
> code|https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala#L320],
>  only two columns are being extracted and fed to the underlying classifiers.. 
> however with some configurations, more than two columns are required.
> Specifically: I want to do multiclass learning with Logistic Regression, on a 
> very imbalanced dataset. In my dataset, I have lots of imbalances, so I was 
> planning to use weights. I set a column, `"weight"`, as the inverse frequency 
> of each field, and I configured my `LogisticRegression` class to use this 
> column, then put it in a `OneVsRest` wrapper.
> However, `OneVsRest` strips all but two columns out of a dataset before 
> training, so I get an error from within `LogisticRegression` that it can't 
> find the `"weight"` column.
> It would be nice to have this fixed! I can see a few ways, but a very 
> conservative fix would be to include a parameter in `OneVsRest.fit` for 
> additional columns to `select` before passing to the underlying model.
> Thanks!



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