Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/7099#issuecomment-117813319
> It wasn't clear to me how to use DFs in the result classes I was creating
since they didn't have access to the model parameters (featuresCol,
predictionCol, etc). I could add them as constructor params if you think
that'll be better but it's not clear to me what the benefit of using a DF in
the result classes is since most use cases will only be interested in the
summary functions rather than the predictions + labels themselves.
Sorry, this was ambiguous. The plan is to have DFs for each result type,
not necessarily ones zipped with the transformed data. Later on, we could
provide extra output columns to include the values in the transformed data, but
we won't just yet. E.g., we can provide a DataFrame storing only 1 column of
residuals.
---
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 [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
