Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/7517#issuecomment-123380348
@jkbradley Isotonic regression expects a single feature instead of a
feature vector. Do we want to make it a `Regressor` and use `featuresCol` as a
param? One common use case of isotonic regression is to calibrate probabilities
output by logistic regression. However, logistic regression only outputs
probabilities as vectors (of size 2). It would be hard to connect logistic
regression with isotonic regression. Any suggestions?
---
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]
