Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/7538#issuecomment-128445916
> For accessing the pr(), fMeasureByThreshold etc, I'll have to do
model.summary.asInstanceOf[Binary..] I suppose that should be okay, right?
(Similar things are being done in LDAModel etc)
I agree it's a bit awkward, but I prefer that to providing null/bad values.
The other big choice we could have made when creating spark.ml is separate
binary and multiclass algorithms, but that would have created a bunch of copied
APIs.
> If I make objectiveHistory and totalIterations defs then, that would be
different from the LinearRegressionSummary where it would be vals. This would
create differences when being called from Java. i.e, I'll have to do
objectiveHistory() for Logistic nd objectiveHistory for Linear
I don't think def and val look different from Java. The Scala compiler
creates both as methods, so they should appear to be the same for the Java and
Scala APIs.
LGTM. Thanks for iterating through updates with me! I'll merge this with
master and branch-1.5
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