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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