[jira] [Closed] (SPARK-5870) GradientBoostedTrees should cache residuals from partial model

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-5870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley closed SPARK-5870.
------------------------------------
       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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