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https://issues.apache.org/jira/browse/SPARK-5870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley closed SPARK-5870.
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Resolution: Duplicate
Fix Version/s: 1.4.0
> GradientBoostedTrees should cache residuals from partial model
> --------------------------------------------------------------
>
> Key: SPARK-5870
> URL: https://issues.apache.org/jira/browse/SPARK-5870
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
> Fix For: 1.4.0
>
>
> On each iteration, GradientBoostedTrees computes predictions for each
> training instance using the partial model. This means it re-computes the
> prediction of each tree on every following iteration, making for
> O(numIterations^2) work instead of O(numIterations).
> It should instead cache the current residuals and update them with the
> predictions from the newest tree on each iteration.
> This will likely speed things up when using small trees (where training trees
> is fastest). For large trees, training may be costly enough to amortize the
> cost of re-computing predictions on each iteration.
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