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From "Seth Hendrickson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4240) Refine Tree Predictions in Gradient Boosting to Improve Prediction Accuracy.
Date Fri, 01 Jul 2016 13:42:11 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4240?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15358983#comment-15358983
] 

Seth Hendrickson commented on SPARK-4240:
-----------------------------------------

I had done some work on this in the past, but haven't looked at it for a while now. I may
have some time to pick it back up again in a few weeks, but if you are interested in working
on it then feel free (please do indicate as such here, though). Thanks!

> Refine Tree Predictions in Gradient Boosting to Improve Prediction Accuracy.
> ----------------------------------------------------------------------------
>
>                 Key: SPARK-4240
>                 URL: https://issues.apache.org/jira/browse/SPARK-4240
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.3.0
>            Reporter: Sung Chung
>
> The gradient boosting as currently implemented estimates the loss-gradient in each iteration
using regression trees. At every iteration, the regression trees are trained/split to minimize
predicted gradient variance. Additionally, the terminal node predictions are computed to minimize
the prediction variance.
> However, such predictions won't be optimal for loss functions other than the mean-squared
error. The TreeBoosting refinement can help mitigate this issue by modifying terminal node
prediction values so that those predictions would directly minimize the actual loss function.
Although this still doesn't change the fact that the tree splits were done through variance
reduction, it should still lead to improvement in gradient estimations, and thus better performance.
> The details of this can be found in the R vignette. This paper also shows how to refine
the terminal node predictions.
> http://www.saedsayad.com/docs/gbm2.pdf



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