Github user sethah 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 --
    If `probability(i)` and `thresholds(i)` are both 0.0 here, we will have 
`scaled = NaN`. Maybe we can break out of the loop early if we encounter a zero 
threshold. BTW, this also begs the question of what the answer should be with 
an infinitely low probability and an infinitely low threshold - but I'm totally 
fine just predicting whatever threshold is zero in that case :D

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