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https://issues.apache.org/jira/browse/SPARK-10578?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley resolved SPARK-10578.
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Resolution: Fixed
Assignee: Joseph K. Bradley
Fix Version/s: 1.5.0
[~viirya] Yep, thanks for pointing out the right link!
> 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: 1.4.0, 1.4.1
> Environment: CentOS, PySpark 1.4.1, Scala 2.10
> Reporter: Karen Yin-Yee Ng
> Assignee: Joseph K. Bradley
> Fix For: 1.5.0
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> 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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