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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-21178) Add support for label specific metrics in MulticlassClassificationEvaluator
Date Thu, 22 Jun 2017 12:14:01 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-21178:
------------------------------------

    Assignee: Apache Spark

> Add support for label specific metrics in MulticlassClassificationEvaluator
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-21178
>                 URL: https://issues.apache.org/jira/browse/SPARK-21178
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.1.1
>            Reporter: Aman Rawat
>            Assignee: Apache Spark
>
> MulticlassClassificationEvaluator is restricted to the global metrics - f1, weightedPrecision,
weightedRecall, accuracy
> However, we have a requirement where we would want to optimize the learning on metric
for a specific label - for instance, true positive rate (label 'B')
> For example : Take a fraud detection use-case with labels 'good' and 'fraud' being passed
to a manual verification team. We want to maximize the true-positive rate of ('fraud') label,
so that whenever the model predicts a data point as 'good', it has a strong likelihood of
it being 'good', and the manual team can ignore it.
> While it's ok to predict some 'good' data points as 'fraud', as it will be taken care
by the manual verification team.



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