Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15435#discussion_r85220020
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -777,6 +787,20 @@ class LogisticRegressionModel private[spark] (
throw new SparkException("No training summary available for this
LogisticRegressionModel")
}
+ @Since("2.1.0")
+ def binarySummary: BinaryLogisticRegressionTrainingSummary = summary
match {
+ case b: BinaryLogisticRegressionTrainingSummary => b
+ case _ => throw new SparkException("Summary type dismatch, " +
+ "BinaryLogisticRegressionTrainingSummary expected.")
+ }
+
+ @Since("2.1.0")
+ def multinomialSummary: MultinomialLogisticRegressionTrainingSummary =
summary match {
+ case m: MultinomialLogisticRegressionTrainingSummary => m
+ case _ => throw new SparkException("Summary type dismatch, " +
--- End diff --
We actually could return a valid multinomial summary, even for binary
problems. I'd recommend we do that.
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