[jira] [Created] (SPARK-32904) pyspark.mllib.evaluation.MulticlassMetrics needs to swap the results of precision( ) and recall( )

["TinaLi (Jira)" <ji...@apache.org>]

TinaLi created SPARK-32904:
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             Summary: pyspark.mllib.evaluation.MulticlassMetrics needs to swap the results of precision( ) and recall( )
                 Key: SPARK-32904
                 URL: https://issues.apache.org/jira/browse/SPARK-32904
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 3.0.1
            Reporter: TinaLi


[https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/evaluation/MulticlassMetrics.html]

*The values returned by the precision() and recall() methods of this API should be swapped.*

Following is the example results I got when I run this API. It prints out precision  
metrics = MulticlassMetrics(predictionAndLabels)
print (metrics.confusionMatrix().toArray())
print ("precision: ",metrics.precision(1))
print ("recall: ",metrics.recall(1))
[[36631. 2845.]

[ 3839. 1610.]]

precision: 0.3613916947250281

recall: 0.2954670581758121

 
predictions.select('prediction').agg(\{'prediction':'sum'}).show()
|sum(prediction)| 5449.0|

As you can see, my model predicted 5449 cases with label=1, and 1610 out of the 5449 cases are true positive, so precision should be  1610/5449=0.2954670581758121, but this API assigned the precision value to recall() method, which should be swapped. 



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