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https://issues.apache.org/jira/browse/SPARK-5885?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15073123#comment-15073123
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Joseph K. Bradley commented on SPARK-5885:
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I'd currently recommend using DataFrame operations to handle null values. To
clarify, is your case that some of your Vectors are null (no vector at all), or
that some elements within a Vector are null? Also, what is the ideal behavior
for VectorAssembler for your use case?
> Add VectorAssembler
> -------------------
>
> Key: SPARK-5885
> URL: https://issues.apache.org/jira/browse/SPARK-5885
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Fix For: 1.4.0
>
>
> `VectorAssembler` takes a list of columns (of type double/int/vector) and
> merge them into a single vector column.
> {code}
> val va = new VectorAssembler()
> .setInputCols("userFeatures", "dayOfWeek", "timeOfDay")
> .setOutputCol("features")
> {code}
> In the first version, it should be okay if it doesn't handle ML attributes
> (SPARK-4588).
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