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https://issues.apache.org/jira/browse/SPARK-6823?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14492661#comment-14492661
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Shivaram Venkataraman commented on SPARK-6823:
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I think the goal of the original JIRA on SparkR was to have a high-level API
that'll allow users to express this . We could have this higher-level API in a
DataFrame or just provide a wrapper around OneHotEncoder + VectorAssembler in
the SparkR ML integration work. I think the second one sounds better to me, but
[~cafreeman] and Dan Putler have been looking at this and might be able to add
more.
> Add a model.matrix like capability to DataFrames (modelDataFrame)
> -----------------------------------------------------------------
>
> Key: SPARK-6823
> URL: https://issues.apache.org/jira/browse/SPARK-6823
> Project: Spark
> Issue Type: New Feature
> Components: ML, SparkR
> Reporter: Shivaram Venkataraman
>
> Currently Mllib modeling tools work only with double data. However, data
> tables in practice often have a set of categorical fields (factors in R),
> that need to be converted to a set of 0/1 indicator variables (making the
> data actually used in a modeling algorithm completely numeric). In R, this is
> handled in modeling functions using the model.matrix function. Similar
> functionality needs to be available within Spark.
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