Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7771#discussion_r35914239
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala ---
@@ -41,6 +42,17 @@ class VectorAssembler(override val uid: String)
def this() = this(Identifiable.randomUID("vecAssembler"))
+ /**
+ * Whether to rewrite vector attribute names.
+ * @group param
+ */
+ final val rewriteAttributeNames: BooleanParam =
--- End diff --
Similar arguments here. It would be nice if we can keep ML attributes under
the hood. I think the major problem is we tied feature name (or feature group
name) with column name, and it is hard to keep good column names during
transformation. If in `OneHotEncoder`, we don't add group name to feature name.
The attribute transformation is
~~~
county -> OneHotEncoder -> country. : ["US", "CA", ...] -> VectorAssembler
-> ["country.US", "country.CA"]
~~~
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