Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/21097#discussion_r184760717
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
---
@@ -365,6 +365,20 @@ class GBTClassifierSuite extends MLTest with
DefaultReadWriteTest {
assert(mostImportantFeature !== mostIF)
}
+ test("model evaluateEachIteration") {
+ for (lossType <- Seq("logistic")) {
+ val gbt = new GBTClassifier()
+ .setMaxDepth(2)
+ .setMaxIter(2)
+ .setLossType(lossType)
+ val model = gbt.fit(trainData.toDF)
+ val eval1 = model.evaluateEachIteration(validationData.toDF)
+ val eval2 =
GradientBoostedTrees.evaluateEachIteration(validationData,
--- End diff --
This is testing the spark.ml implementation against itself. I was about to
recommend using the old spark.mllib implementation as a reference. However,
the old implementation is not tested at all. Would you be able to test against
a standard implementation in R or scikit-learn (following the patterns used
elsewhere in MLlib)?
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