Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15149#discussion_r80070804
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
---
@@ -200,22 +200,17 @@ abstract class ProbabilisticClassificationModel[
if (!isDefined(thresholds)) {
probability.argmax
} else {
- val thresholds: Array[Double] = getThresholds
- val probabilities = probability.toArray
+ val thresholds = getThresholds
var argMax = 0
var max = Double.NegativeInfinity
var i = 0
val probabilitySize = probability.size
while (i < probabilitySize) {
- if (thresholds(i) == 0.0) {
- max = Double.PositiveInfinity
+ // thresholds are all > 0, excepting that at most one may be 0
+ val scaled = probability(i) / thresholds(i)
+ if (scaled > max) {
--- End diff --
You're right. It will never be predicted, and I think that's more sensible
because probability = 0 means "never predict" and 0 threshold only sort of
_implies_ always predicting the class. It's undefined, so either one seems
coherent as a result. I prefer the current behavior I guess.
I see it's possible code-wise to have one class but don't think it's a
valid use case, so, not worried about the behavior (even if it will still
return the one single class here always anyway).
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