Zak Patterson created SPARK-18844: ------------------------------------- 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org