Aman Rawat created SPARK-21178:
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Summary: 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
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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