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https://issues.apache.org/jira/browse/SPARK-5436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14294360#comment-14294360
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Joseph K. Bradley commented on SPARK-5436:
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Yes, it would be reasonable to take the same Loss (metric) which GBT tries to
minimize on the training set and re-use that Loss for validation. (Eventually,
we could let the user specify a different metric, but I vote for keeping it
simple for now.)
> Validate GradientBoostedTrees during training
> ---------------------------------------------
>
> Key: SPARK-5436
> URL: https://issues.apache.org/jira/browse/SPARK-5436
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> For Gradient Boosting, it would be valuable to compute test error on a
> separate validation set during training. That way, training could stop early
> based on the test error (or some other metric specified by the user).
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