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https://issues.apache.org/jira/browse/SPARK-5972?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14338930#comment-14338930
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Joseph K. Bradley commented on SPARK-5972:
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Yes, but it also includes the gradient computation. Each tree should only have
to predict on each training instance once.
> 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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