[spark] branch master updated: [MINOR][DOC] Updated PySpark Binarizer docstring to match Scala's.

[sr...@apache.org]

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The following commit(s) were added to refs/heads/master by this push:
     new 340c8b8  [MINOR][DOC] Updated PySpark Binarizer docstring to match Scala's.
340c8b8 is described below

commit 340c8b8387bbaebdd23958c9ee7a352461abc1f8
Author: Brooke Wenig <br...@gmail.com>
AuthorDate: Wed Mar 6 19:42:41 2019 -0600

    [MINOR][DOC] Updated PySpark Binarizer docstring to match Scala's.
    
    ## What changes were proposed in this pull request?
    
    PySpark's Binarizer docstring had two issues:
    1) The values did not need to be in the range [0, 1].
    2) It can be used for binary classification prediction.
    
    This change corrects both of these issues by making it consistent with Scala's docstring for Binarizer.
    
    ## How was this patch tested?
    
    Not applicable because I only changed the docstring. But if I need to do any testing, let me know and I'll do it.
    
    Please review http://spark.apache.org/contributing.html before opening a pull request.
    
    Closes #23934 from brookewenig/binarizer-docs-fix.
    
    Authored-by: Brooke Wenig <br...@gmail.com>
    Signed-off-by: Sean Owen <se...@databricks.com>
---
 python/pyspark/ml/feature.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8583046..3f9de9c 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -86,7 +86,9 @@ class Binarizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Java
     """
 
     threshold = Param(Params._dummy(), "threshold",
-                      "threshold in binary classification prediction, in range [0, 1]",
+                      "Param for threshold used to binarize continuous features. " +
+                      "The features greater than the threshold will be binarized to 1.0. " +
+                      "The features equal to or less than the threshold will be binarized to 0.0",
                       typeConverter=TypeConverters.toFloat)
 
     @keyword_only


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