Zak Patterson created SPARK-18844:
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Summary: Add more binary classification metrics to
BinaryClassificationMetrics
Key: SPARK-18844
URL: https://issues.apache.org/jira/browse/SPARK-18844
Project: Spark
Issue Type: Improvement
Components: MLlib
Affects Versions: 2.0.2
Reporter: Zak Patterson
Priority: Minor
Fix For: 2.0.2
BinaryClassificationMetrics only implements Precision (positive predictive
value) and recall (true positive rate). It should implement more comprehensive
metrics.
Moreover, the instance variables storing computed counts are marked private,
and there are no accessors for them. So if one desired to add this
functionality, one would have to duplicate this calculation, which is not
trivial:
https://github.com/apache/spark/blob/v2.0.2/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala#L144
Currently Implemented Metrics
---
* Precision (PPV): `precisionByThreshold`
* Recall (Sensitivity, true positive rate): `recallByThreshold`
Desired additional metrics
---
* False omission rate: `fprByThreshold`
* False discovery rate: `fdrByThreshold`
* Negative predictive value: `npvByThreshold`
* False negative rate: `fnrByThreshold`
* True negative rate (Specificity): `specificityByThreshold`
* False positive rate: `fprByThreshold`
Alternatives
---
The `createCurve` method is marked private. If it were marked public, and the
trait BinaryClassificationMetricComputer were also marked public, then it would
be easy to define new computers to get whatever the user wanted.
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