Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15435
So I've been reading through some of the history with logistic regression
summaries. There was a lot of discussion on how to design the abstractions for
this, [here](https://github.com/apache/spark/pull/7538) and
[here](https://github.com/apache/spark/pull/8197).
I'm reposting some of the relevant snippets (I will comment on them in a
follow up):
"We'll need to use traits to fix the multiple inheritance issue:"
````
sealed trait LogisticRegressionSummary
sealed trait LogisticRegressionTrainingSummary
class BinaryLogisticRegressionSummary extends LogisticRegressionSummary
class BinaryLogisticRegressionTrainingSummary extends
BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary
````
"Are we planning to have a MulticlassLogisticRegressionSummary inheriting
from LogisticRegressionSummary in the future because without that I'm unable to
understand how using a trait would help since there is no access to the
predictions dataframe."
"Yes, MulticlassLogisticRegressionSummary should be analogous to the binary
version, with both inheriting from LogisticRegressionSummary."
...
"Synced with @jkbradley offline. Summary:
We should not require end users to perform any sort of downcasting in the
stabilized API. This is OK for now since the API is still experimental.
Eventually we could provide two methods, a summary :
LogisticRegressionSummary and a binarySummary : BInaryLogisticRegressionSummary
which errors when called on a multiclass LRModel. This will be easy to
implement because summary is returning the base LogisticRegressionSummary class
so will not require any public API change."
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