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From jkbradley <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-9112] [ML] Implement Stats for Logistic...
Date Thu, 06 Aug 2015 17:07:43 GMT
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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