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https://issues.apache.org/jira/browse/SPARK-5888?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14541354#comment-14541354
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Xiangrui Meng commented on SPARK-5888:
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The values of an nominal attribute is an Option. So it would be None, which
means unknown. If `OneHotEncoder.transformSchema` reads in an empty nominal
attribute, it should assume that this would be correct and append a vector type
column to the input schema. However, this logic has not been carefully
examined, which is part of the 1.4 QA.
> Add OneHotEncoder as a Transformer
> ----------------------------------
>
> Key: SPARK-5888
> URL: https://issues.apache.org/jira/browse/SPARK-5888
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Sandy Ryza
> Fix For: 1.4.0
>
>
> `OneHotEncoder` takes a categorical column and output a vector column, which
> stores the category info in binaries.
> {code}
> val ohe = new OneHotEncoder()
> .setInputCol("countryIndex")
> .setOutputCol("countries")
> {code}
> It should read the category info from the metadata and assign feature names
> properly in the output column. We need to discuss the default naming scheme
> and whether we should let it process multiple categorical columns at the same
> time.
> One category (the most frequent one) should be removed from the output to
> make the output columns linear independent. Or this could be an option tuned
> on by default.
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