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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