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https://issues.apache.org/jira/browse/SPARK-5436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14323248#comment-14323248
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Joseph K. Bradley commented on SPARK-5436:
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I think it would be nice to have a stopping criterion, but it should be more
like a convergence tolerance than a target error rate (since that can't be
known a priori, as [~ChrisT] said). The test error of each iteration's model
should be compared with the error from the previous iteration. If it ever
decreases by less than convergenceTol, then we stop. I'd vote for 0 or
something small like 1e-5 for a default value. How does that sound?
> 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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