mgaido91 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_r257454753
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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:
Well, the seed is fixed, so the UT is actually deterministic and there is no
flakyness. Despite with a different seed the result may be different, I'd
consider very unlikely anyway that 1 would not be the most important one in any
case, since it is really the ground truth in this case.
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