[SPARK-17057][ML] ProbabilisticClassifierModels' thresholds should have at most one 0

## What changes were proposed in this pull request?

Match ProbabilisticClassifer.thresholds requirements to R randomForest cutoff, requiring all > 0

## How was this patch tested?

Jenkins tests plus new test cases

Author: Sean Owen <sowen@cloudera.com>

Closes #15149 from srowen/SPARK-17057.

1 files changed, 2 insertions, 2 deletions

diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 24af07afc7..cc596936d8 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -469,10 +469,10 @@ class HasStandardization(Params):
 
 class HasThresholds(Params):
     """
-    Mixin for param thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.
+    Mixin for param thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
     """
 
-    thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", typeConverter=TypeConverters.toListFloat)
+    thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.", typeConverter=TypeConverters.toListFloat)
 
     def __init__(self):
         super(HasThresholds, self).__init__()