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_r257307844
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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:
OK, I get it, we just expect something different to happen under the hood,
even if we're largely expecting a similar or the same answer. Leave it in; if
it failed because it exactly matched, we'd know it, and could easily figure out
whether that's actually now expected or a bug.
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