diff options
author | Sean Owen <sowen@cloudera.com> | 2016-09-24 08:15:55 +0100 |
---|---|---|
committer | Sean Owen <sowen@cloudera.com> | 2016-09-24 08:15:55 +0100 |
commit | 248916f5589155c0c3e93c3874781f17b08d598d (patch) | |
tree | 4e3183ffc5d59e09edc8b54ddc2af4fc67abb05b /python/pyspark/ml/param/shared.py | |
parent | f3fe55439e4c865c26502487a1bccf255da33f4a (diff) | |
download | spark-248916f5589155c0c3e93c3874781f17b08d598d.tar.gz spark-248916f5589155c0c3e93c3874781f17b08d598d.tar.bz2 spark-248916f5589155c0c3e93c3874781f17b08d598d.zip |
[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.
Diffstat (limited to 'python/pyspark/ml/param/shared.py')
-rw-r--r-- | python/pyspark/ml/param/shared.py | 4 |
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__() |