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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