Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/20829#discussion_r177501836
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala ---
@@ -49,32 +53,57 @@ class VectorAssembler @Since("1.4.0") (@Since("1.4.0")
override val uid: String)
@Since("1.4.0")
def setOutputCol(value: String): this.type = set(outputCol, value)
+ /** @group setParam */
+ @Since("2.4.0")
+ def setHandleInvalid(value: String): this.type = set(handleInvalid,
value)
+
+ /**
+ * Param for how to handle invalid data (NULL values). Options are
'skip' (filter out rows with
--- End diff --
I'd recommend we deal with NaNs now. This PR is already dealing with some
NaN cases: Dataset.na.drop handles NaNs in NumericType columns (but not
VectorUDT columns).
I'm Ok with postponing incorrect vector lengths until later or doing that
now since that work will be more separate.
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