eneriwrt created SPARK-28222:
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Summary: 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: 2.4.3, 2.4.2, 2.4.1, 2.4.0
Reporter: eneriwrt
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.
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]
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