Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/3305#discussion_r20458024
--- Diff: python/pyspark/mllib/classification.py ---
@@ -29,47 +30,96 @@
'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
-class LogisticRegressionModel(LinearModel):
+class ClassificationModel(LinearModel):
+ """
+ :: Experimental ::
+
+ Represents a classification model that predicts to which of a set of
categories an example
+ belongs. The categories are represented by double values: 0.0, 1.0,
2.0, etc.
+ """
+ def __init__(self, weights, intercept):
+ super(ClassificationModel, self).__init__(weights, intercept)
+ self._threshold = 0.5
--- End diff --
We have these defaults in Scala, do we also should update them in Scala?
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