Github user jaceklaskowski commented on a diff in the pull request: https://github.com/apache/spark/pull/12274#discussion_r59124592 --- Diff: mllib/src/main/scala/org/apache/spark/ml/Predictor.scala --- @@ -171,18 +171,18 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, * @param dataset input dataset * @return transformed dataset with [[predictionCol]] of type [[Double]] */ - override def transform(dataset: DataFrame): DataFrame = { + override def transform(dataset: Dataset[_]): DataFrame = { --- End diff -- What about the return type? If I want to chain transformers by `andThen`, i.e. `(tok.transform _).andThen(hashTF.transform)`, won't `DataFrame` be an issue? Why do we keep `DataFrame` as the return type?
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org