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Marco Gaido commented on SPARK-28222: ------------------------------------- [~eneriwrt] do you have a simple repro for this? I can try and check it if I have an example to debug. > Feature importance outputs different values in GBT and Random Forest in 2.3.3 > and 2.4 pyspark version > ----------------------------------------------------------------------------------------------------- > > Key: SPARK-28222 > URL: https://issues.apache.org/jira/browse/SPARK-28222 > Project: Spark > Issue Type: Bug > Components: ML > Affects Versions: 2.4.0, 2.4.1, 2.4.2, 2.4.3 > Reporter: eneriwrt > Priority: Minor > > Feature importance values obtained in a binary classification project outputs > different values if 2.3.3 version used or 2.4.0. It happens in Random Forest > and GBT. Turns out that values that are equal than sklearn output are from > 2.3.3 version. > As an example: > *SPARK 2.4* > MODEL RandomForestClassifier_gini [0.0, 0.4117930839002269, > 0.06894132653061226, 0.15857667209786705, 0.2974447311021076, > 0.06324418636918638] > MODEL RandomForestClassifier_entropy [0.0, 0.3864372497988694, > 0.06578883597468652, 0.17433924485055197, 0.31754597164210124, > 0.055888697733790925] > MODEL GradientBoostingClassifier [0.0, 0.7555555555555556, > 0.24444444444444438, 0.0, 1.4602196686471875e-17, 0.0] > *SPARK 2.3.3* > MODEL RandomForestClassifier_gini [0.0, 0.40957086167800455, > 0.06894132653061226, 0.16413222765342259, 0.2974447311021076, > 0.05991085303585305] > MODEL RandomForestClassifier_entropy [0.0, 0.3864372497988694, > 0.06578883597468652, 0.18789704501922055, 0.30398817147343266, > 0.055888697733790925] > MODEL GradientBoostingClassifier [0.0, 0.7555555555555555, > 0.24444444444444438, 0.0, 2.4326753518951276e-17, 0.0] -- This message was sent by Atlassian JIRA (v7.6.14#76016) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org