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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/05/18 21:14:01 UTC
[jira] [Commented] (SPARK-7690) MulticlassClassificationEvaluator
for tuning Multiclass Classifiers
[ https://issues.apache.org/jira/browse/SPARK-7690?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14548577#comment-14548577 ]
Joseph K. Bradley commented on SPARK-7690:
------------------------------------------
+1
We should also check for implementations in other packages to find out what arguments and argument names (including "micro" and "macro") are most common. Perhaps R, Weka, etc.
> MulticlassClassificationEvaluator for tuning Multiclass Classifiers
> -------------------------------------------------------------------
>
> Key: SPARK-7690
> URL: https://issues.apache.org/jira/browse/SPARK-7690
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Ram Sriharsha
> Assignee: Ram Sriharsha
>
> Provide a MulticlassClassificationEvaluator with weighted F1-score to tune multiclass classifiers using Pipeline API.
> MLLib already provides a MulticlassMetrics functionality which can be wrapped around a MulticlassClassificationEvaluator to expose weighted F1-score as metric.
> The functionality could be similar to scikit(http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) in that we can support micro, macro and weighted versions of the F1-score (with weighted being default)
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