[
https://issues.apache.org/jira/browse/SPARK-5436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14294403#comment-14294403
]
Chris T commented on SPARK-5436:
--------------------------------
I think, then, the only addition needed is to retain the mean loss on every
iteration. This is computed and emitted to the log on each build iteration:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala#L179
The question then becomes where to store this error value. Is it a property of
the tree or the model? For a DecisionTree, I can see how the concept of the
error applies. For a random forest, since each tree is independent of the
others, that also makes sense. But for a GBT model, the model for N trees is
dependent on the model with N-1 trees, so if I extract the Nth tree and request
the error value, I have to be aware that this is not the error for this tree
alone. I suspect this is fine...anyone building a GBT model would likely
understand this. It's jsut a little weird to store a property of an object that
is dependent on other objects in the ensemble.
> 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).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
