[
https://issues.apache.org/jira/browse/SPARK-5972?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joseph K. Bradley resolved SPARK-5972.
--------------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
Issue resolved by pull request 5330
[https://github.com/apache/spark/pull/5330]
> Cache residuals for GradientBoostedTrees during training
> --------------------------------------------------------
>
> Key: SPARK-5972
> URL: https://issues.apache.org/jira/browse/SPARK-5972
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
> Assignee: Manoj Kumar
> Priority: Minor
> Fix For: 1.4.0
>
>
> In gradient boosting, the current model's prediction is re-computed for each
> training instance on every iteration. The current residual (cumulative
> prediction of previously trained trees in the ensemble) should be cached.
> That could reduce both computation (only computing the prediction of the most
> recently trained tree) and communication (only sending the most recently
> trained tree to the workers).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
