Github user srowen commented on the issue:
https://github.com/apache/spark/pull/16654
Sure, and classification metrics like AUC only make sense for classifiers
that output more than just a label -- they have to output a probability or
score of some kind. Not every metric necessarily makes sense for every model,
and we can use class hierarchy or just argument checking to avoid applying
metrics where nonsensical. WSSSE can't be used for k-medoids, yes. k-medoids is
also not in Spark, AFAIK. It's still not an argument to not abstract this at
all.
---
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]
