[
https://issues.apache.org/jira/browse/SPARK-16718?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15392662#comment-15392662
]
Joseph K. Bradley commented on SPARK-16718:
-------------------------------------------
Also, it'd be nice to compare with an existing implementation. E.g., if we can
compare with R gbm, we can add a unit test doing that, following a few other
unit tests in spark.ml.
Note: [~vlad.feinberg] is working on this now.
> gbm-style treeboost
> -------------------
>
> Key: SPARK-16718
> URL: https://issues.apache.org/jira/browse/SPARK-16718
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib
> Reporter: Vladimir Feinberg
>
> As an initial minimal change, we should provide TreeBoost as implemented in
> GBM for both L1 and L2 losses: by introducing a new "loss-based" impurity,
> tree leafs in GBTs can have loss-optimal predictions for their partition of
> the data.
> Commit should have evidence of accuracy improvment
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
