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Chris T commented on SPARK-5436: -------------------------------- That sounds like a good idea to me, with the caveat that if the convergenceTolerance was set to 0, then the algorithm runs until the full number of boosting iterations has been reached. This way users could iterate until convergence, or just build a model with N trees. Both seem like reasonable use-cases. > 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