[GitHub] srowen commented on a change in pull request #23773: [SPARK-26721][ML] Avoid per-tree normalization in featureImportance for GBT

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srowen commented on a change in pull request #23773: [SPARK-26721][ML] Avoid per-tree normalization in featureImportance for GBT
URL: https://github.com/apache/spark/pull/23773#discussion_r257298566
 
 

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 File path: mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
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 @@ -363,7 +363,8 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
     val gbtWithFeatureSubset = gbt.setFeatureSubsetStrategy("1")
     val importanceFeatures = gbtWithFeatureSubset.fit(df).featureImportances
     val mostIF = importanceFeatures.argmax
-    assert(mostImportantFeature !== mostIF)
+    assert(mostIF === 1)
 
 Review comment:
   Yeah I should have commented on this; I actually don't know why the test previously asserted the answers must be different. That's actually the thing I'd least expect, though it's possible. Why does it still assert the importances are different? I suspect they won't match exactly, sure, but if there's an assertion here, isn't it that they're close? They may just not be that comparable in which case there's nothing to assert.

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