Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/20829#discussion_r175908248 --- 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 -- It would be good to expand this doc to explain the behavior: how various types of invalid values are treated (null, NaN, incorrect Vector length) and how computationally expensive different options can be.
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