[jira] [Created] (SPARK-21306) OneVsRest Conceals Columns That May Be Relevant To Underlying Classifier

["Cathal Garvey (JIRA)" <ji...@apache.org>]

Cathal Garvey created SPARK-21306:
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             Summary: 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: Improvement
          Components: ML
    Affects Versions: 2.1.1
            Reporter: Cathal Garvey
            Priority: Minor


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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