[jira] [Commented] (SPARK-1227) Diagnostics for Classification&Regression

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-1227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14203354#comment-14203354 ] 

Sean Owen commented on SPARK-1227:
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Is this still relevant now that conventional classifier and regressor metrics are implemented in MLlib? You wouldn't be able to compare models by their loss function in general anyway.

> Diagnostics for Classification&Regression
> -----------------------------------------
>
>                 Key: SPARK-1227
>                 URL: https://issues.apache.org/jira/browse/SPARK-1227
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Martin Jaggi
>            Assignee: Martin Jaggi
>
> Currently, the attained objective function is not computed (for efficiency reasons, as one evaluation requires one full pass through the data).
> For diagnostics and comparing different algorithms, we should however provide this as a separate function (one MR).
> Doing this requires the loss and regularizer functions themselves, not only their gradients (which are currently in the Gradient class). How about adding the new function directly on the corresponding models in classification/* and regression/* ? Any thoughts?



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