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