Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16441#discussion_r95418341
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala ---
@@ -275,6 +321,13 @@ class GBTClassificationModel private[ml](
@Since("2.0.0")
lazy val featureImportances: Vector =
TreeEnsembleModel.featureImportances(trees, numFeatures)
+ private def classProbability(loss: String, rawPrediction: Double):
Double = {
--- End diff --
adding "private val oldLoss: ClassificationLoss = getOldLossType" won't
work because getOldLossType returns a Loss and not a LogLoss, which doesn't
have computeProbability. However, I did add the ClassificationLoss trait and
in ClassProbability I just call LogLoss.computeProbability. I'm not sure if it
will pass the binary compat checks though, let's see...
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