I know some implementations of model save/load in MLlib use an explicit version 1.0, 2.0, 3.0 mechanism. I've also seen that some just decide based on the version of Spark that wrote the model.
Is one or the other preferred? See https://github.com/apache/spark/pull/23549#discussion_r248318392 for example. In cases like this, is it simpler still to just select all the values written in the model and decide what to do based on the presence or absence of columns? That seems a little more robust. It wouldn't be so much an option if the contents or meaning of the columns had changed. --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
