[jira] [Commented] (SPARK-3727) Trees and ensembles: More prediction functionality

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14492928#comment-14492928 ] 

Joseph K. Bradley commented on SPARK-3727:
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[~maxkaznady] [~mqk] I split this into some subtasks, and we can add others later (for boosted trees, for regression, etc.).  It will be great if you can follow the spark.ml tree API JIRA (linked above) and take a look at it once it's posted.  That (and the ProbabilisticClassifier class) will give you an idea of what's entailed in adding these under the Pipelines API.

Do you have preferences on how to split up these tasks?  If you can figure that out, I'll be happy to assign them.  Thanks!

> Trees and ensembles: More prediction functionality
> --------------------------------------------------
>
>                 Key: SPARK-3727
>                 URL: https://issues.apache.org/jira/browse/SPARK-3727
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>
> DecisionTree and RandomForest currently predict the most likely label for classification and the mean for regression.  Other info about predictions would be useful.
> For classification: estimated probability of each possible label
> For regression: variance of estimate
> RandomForest could also create aggregate predictions in multiple ways:
> * Predict mean or median value for regression.
> * Compute variance of estimates (across all trees) for both classification and regression.



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