[jira] [Created] (SPARK-10578) pyspark.ml.classification.RandomForestClassifer does not return `rawPrediction` column

["Karen Yin-Yee Ng (JIRA)" <ji...@apache.org>]

Karen Yin-Yee Ng created SPARK-10578:
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             Summary: pyspark.ml.classification.RandomForestClassifer does not return `rawPrediction` column
                 Key: SPARK-10578
                 URL: https://issues.apache.org/jira/browse/SPARK-10578
             Project: Spark
          Issue Type: Bug
          Components: ML
    Affects Versions: 1.4.1, 1.4.0
         Environment: CentOS, PySpark 1.4.1, Scala 2.10 
            Reporter: Karen Yin-Yee Ng


To use `pyspark.ml.classification.RandomForestClassifer` with `BinaryClassificationEvaluator`, a column called `rawPrediction` needs to be returned by the `RandomForestClassifer`. 
The PySpark documentation example of `logisticsRegression`outputs the `rawPrediction` column but not `RandomForestClassifier`.

Therefore, one is unable to use `RandomForestClassifier` with the evaluator nor put it in a pipeline with cross validation.

A relevant piece of code showing how to reproduce the bug can be found at:
https://gist.github.com/karenyyng/cf61ae655b032f754bfb

A relevant post due to this possible bug can also be found at:
http://apache-spark-user-list.1001560.n3.nabble.com/Issue-with-running-CrossValidator-with-RandomForestClassifier-on-dataset-td23791.html





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