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https://issues.apache.org/jira/browse/SPARK-5436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14323323#comment-14323323
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Joseph K. Bradley commented on SPARK-5436:
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Yep, that sounds like what I had in mind:
{code}
def evaluateEachIteration(data: RDD[LabeledPoint], evaluator or maybe use
training metric): Array[Double]
{code}
where it essentially calls predict() once but keeps the intermediate results
after each boosting stage, so that it runs in the same big-O time as predict().
> 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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