You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:21:28 UTC
[jira] [Updated] (SPARK-14606) Different maxBins value for
categorical and continuous features in RandomForest implementation.
[ https://issues.apache.org/jira/browse/SPARK-14606?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-14606:
---------------------------------
Labels: bulk-closed (was: )
> 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
> Labels: bulk-closed
>
> 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.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org