Github user srowen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15149#discussion_r80066262
  
    --- 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 --
    
    Yeah, that occurred to me. It will never be selected because NaN isn't 
bigger than anything, including NegativeInfinity. If for some reason you have 
one class only (is this even valid?) you'd select this class, which is I guess 
correct.
    
    Very small but positive prob / threshold? that should still work fine to 
the limits of machine precision here.


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