sam created SPARK-8375:
--------------------------
Summary: BinaryClassificationMetrics in ML Lib has odd API
Key: SPARK-8375
URL: https://issues.apache.org/jira/browse/SPARK-8375
Project: Spark
Issue Type: Bug
Components: MLlib
Reporter: sam
According to
https://spark.apache.org/docs/1.0.0/api/scala/index.html#org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
The constructor takes `RDD[(Double, Double)]` which does not make sense it
should be `RDD[(Double, T)]` or at least `RDD[(Double, Int)]`.
In scikit I believe they use the number of unique scores to determine the
number of thresholds and the ROC. I assume this is what
BinaryClassificationMetrics is doing since it makes no mention of buckets. In
a Big Data context this does not make as the number of unique scores may be
huge.
Rather user should be able to either specify the number of buckets, or the
number of data points in each bucket. E.g. `def roc(numPtsPerBucket: Int)`
Finally it would then be good if either the ROC output type was changed or
another method was added that returned confusion matricies, so that the hard
integer values can be obtained. E.g.
```
case class Confusion(tp: Int, fp: Int, fn: Int, tn: Int) {
// bunch of methods for each of the things in the table here
https://en.wikipedia.org/wiki/Receiver_operating_characteristic
}
...
def confusions(numPtsPerBucket: Int): RDD[Confusion]
```
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