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Joseph K. Bradley commented on SPARK-11730: ------------------------------------------- I wrote that note since I did not have time to research what people do for GBTs. I'd be Ok with matching sklearn's implementation, though it would be great if we could find academic work indicating a "right" way to handle GBTs. In particular, I am not sure if trees' contributions should be weighted differently (based on the learning process) or if they should just use the tree weights (resembling how prediction works). > Feature Importance for GBT > -------------------------- > > Key: SPARK-11730 > URL: https://issues.apache.org/jira/browse/SPARK-11730 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib > Reporter: Brian Webb > > Random Forests have feature importance, but GBT do not. It would be great if > we can add feature importance to GBT as well. Perhaps the code in Random > Forests can be refactored to apply to both types of ensembles. > See https://issues.apache.org/jira/browse/SPARK-5133 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org