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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) -- 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