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https://issues.apache.org/jira/browse/SPARK-5436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14294523#comment-14294523
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Joseph K. Bradley commented on SPARK-5436:
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That sound good. I think the main "challenge" in this JIRA is specifying the
API for passing 2 datasets to the algorithm instead of 1. Basically, it will
be good to make sure that other algorithms can follow a similar API. Some
possibilities are:
* Pass in a pair of RDDs, one for training and one for validation.
* Pass in 1 RDD, plus parameters for how to select a random subsample for
validation.
I vote for the first option since it is more flexible than the 2nd.
Another question is whether to pass in a separate validation metric. I vote
for not allowing this since the API could always be extended later on.
So...it sounds like a simple API but may get some discussion from other
reviewers.
Would you be interested in working on this?
> 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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