Github user srowen commented on a diff in the pull request:
    --- Diff: 
    @@ -200,22 +200,17 @@ abstract class ProbabilisticClassificationModel[
         if (!isDefined(thresholds)) {
         } 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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