[
https://issues.apache.org/jira/browse/SPARK-21178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen resolved SPARK-21178.
-------------------------------
Resolution: Duplicate
> 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
> Priority: Major
> Labels: bulk-closed
>
> 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.
--
This message was sent by Atlassian Jira
(v8.3.2#803003)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
