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https://issues.apache.org/jira/browse/SPARK-26721?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Takeshi Yamamuro updated SPARK-26721:
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Priority: Major (was: Blocker)
> Bug in feature importance calculation in GBM (and possibly other decision
> tree classifiers)
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-26721
> URL: https://issues.apache.org/jira/browse/SPARK-26721
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.4.0
> Reporter: Daniel Jumper
> Priority: Major
>
> The feature importance calculation in
> org.apache.spark.ml.classification.GBTClassificationModel.featureImportances
> follows a flawed implementation from scikit-learn resulting in incorrect
> importance values. This error was recently discovered and updated in
> scikit-learn version 0.20.0. This error is inherited in the spark
> implementation and needs to be fixed here as well.
> As described in the scikit-learn release notes
> ([https://scikit-learn.org/stable/whats_new.html#version-0-20-0]):
> {quote}Fix Fixed a bug in ensemble.GradientBoostingRegressor and
> ensemble.GradientBoostingClassifier to have feature importances summed and
> then normalized, rather than normalizing on a per-tree basis. The previous
> behavior over-weighted the Gini importance of features that appear in later
> stages. This issue only affected feature importances. #11176 by Gil Forsyth.
> {quote}
> Full discussion of this error and debate ultimately validating the
> correctness of the change can be found in the comment thread of the
> scikit-learn pull request:
> [https://github.com/scikit-learn/scikit-learn/pull/11176]
>
> I believe the main change required would be to the featureImportances
> function in mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala ,
> however, I do not have the experience to make this change myself.
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