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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2017/01/18 23:34:27 UTC
[jira] [Resolved] (SPARK-14975) Predicted Probability per training
instance for Gradient Boosted Trees
[ https://issues.apache.org/jira/browse/SPARK-14975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley resolved SPARK-14975.
---------------------------------------
Resolution: Fixed
Fix Version/s: 2.2.0
Issue resolved by pull request 16441
[https://github.com/apache/spark/pull/16441]
> Predicted Probability per training instance for Gradient Boosted Trees
> ----------------------------------------------------------------------
>
> Key: SPARK-14975
> URL: https://issues.apache.org/jira/browse/SPARK-14975
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Partha Talukder
> Assignee: Ilya Matiach
> Priority: Minor
> Labels: mllib
> Fix For: 2.2.0
>
>
> This function available for Logistic Regression, SVM etc. (model.setThreshold()) but not for GBT. In comparison to "gbm" package in R, where we can specify the distribution and get predicted probabilities or classes. I understand that this algorithm works with "Classification" and "Regression" algo's. Is there any way where in GBT we can get predicted probabilities or provide thresholds to the model?
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