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https://issues.apache.org/jira/browse/SPARK-14606?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-14606:
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Fix Version/s: (was: 2.0.0)
> Different maxBins value for categorical and continuous features in
> RandomForest implementation.
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-14606
> URL: https://issues.apache.org/jira/browse/SPARK-14606
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Reporter: Rahul Tanwani
> Priority: Minor
>
> Currently the RandomForest algo takes a single maxBins value to decide the
> number of splits to take. This sometimes causes training time to go very high
> when there is a single categorical column having sufficiently large number of
> unique values. This single column impacts all the numeric (continuous)
> columns even though such a high number of splits are not required.
> Encoding the categorical column into features make the data very wide and
> this requires us to increase the maxMemoryInMB and puts more pressure on the
> GC as well.
> Keeping the separate maxBins values for categorial and continuous features
> should be useful in this regard.
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