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

    https://github.com/apache/spark/pull/15149#discussion_r80072011
  
    --- 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 --
    
    Ok, I think that's reasonable behavior. Is it better to handle the zero 
threshold case in the code explicitly? It confused me at first, and I had to 
refer to divide by zero behavior in scala to understand the code.


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