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https://issues.apache.org/jira/browse/SPARK-5972?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14338869#comment-14338869
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Manoj Kumar edited comment on SPARK-5972 at 2/26/15 6:30 PM:
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[~josephkb]
Just to clarify, this is just to prevent recomputation of the error from
previous trees in the computeError methods right?. More specifically lines like
these.
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L198
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L203
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L233
Or is there anything more to this?
was (Author: mechcoder):
Just to clarify, this is just to prevent recomputation of the error from
previous trees in the computeError methods right?. More specifically lines like
these.
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L198
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L203
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L233
Or is there anything more to this?
> 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
> Priority: Minor
>
> 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).
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