Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15149#discussion_r79412702
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
---
@@ -56,6 +56,21 @@ class ProbabilisticClassifierSuite extends SparkFunSuite
{
val testModel = new TestProbabilisticClassificationModel("myuid", 2, 2)
assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 2.0))) ===
1.0)
}
+
+ test("test tiebreak") {
+ val testModel = new TestProbabilisticClassificationModel("myuid", 2, 2)
+ .setThresholds(Array(0.4, 0.4))
+ assert(testModel.friendlyPredict(Vectors.dense(Array(0.6, 0.6))) ===
0.0)
+ }
+
+ test("bad thresholds") {
+ intercept[IllegalArgumentException] {
--- End diff --
Sounds good.
BTW I'm finding that many cases use thresholds that sum to 1. Is it
actually important to prohibit this? I don't see that thresholds/cutoffs are
actually interpreted as a probability distribution or anything.
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