Github user sethah commented on the issue:
    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]( and 
    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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