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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