imatiach-msft commented on a change in pull request #17084:
[SPARK-24103][ML][MLLIB] ML Evaluators should use weight column - added weight
column for binary classification evaluator
URL: https://github.com/apache/spark/pull/17084#discussion_r257111949
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
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@@ -41,9 +41,18 @@ import org.apache.spark.sql.DataFrame
* partition boundaries.
*/
@Since("1.0.0")
-class BinaryClassificationMetrics @Since("1.3.0") (
- @Since("1.3.0") val scoreAndLabels: RDD[(Double, Double)],
- @Since("1.3.0") val numBins: Int) extends Logging {
+class BinaryClassificationMetrics @Since("3.0.0") (
+ @Since("1.3.0") val scoreAndLabelsWithOptWeight: RDD[_ <: Product],
Review comment:
no we currently do not check for the weight > 0, should that live in
BinaryClassificationMetrics? I should probably update the other metrics
classes as well then?
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