If you create your own Spark 2.x ML Transformer, there are multiple mix-ins (is that the correct term?) that you can use to define its behavior which are in ml/param/shared.py <https://github.com/apache/spark/blob/master/python/pyspark/ml/param/shared.py> .
Among them are the following mix-ins: - HasInputCol - HasInputCols - HasOutputCol What’s *not* available is a HasOutputCols mix-in, and I assume that is intentional. Is there a design reason why Transformers should not be able to define multiple output columns? I’m guessing if you are an ML beginner who thinks they need a Transformer with multiple output columns, you’ve misunderstood something. 😅 Nick
