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Posted to issues@spark.apache.org by "Aman Omer (Jira)" <ji...@apache.org> on 2019/09/25 12:45:00 UTC
[jira] [Commented] (SPARK-29232) RandomForestRegressionModel does
not update the parameter maps of the DecisionTreeRegressionModels
underneath
[ https://issues.apache.org/jira/browse/SPARK-29232?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16937694#comment-16937694 ]
Aman Omer commented on SPARK-29232:
-----------------------------------
I would like to analyse this.
Thanks [~jiaqig]
> RandomForestRegressionModel does not update the parameter maps of the DecisionTreeRegressionModels underneath
> -------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-29232
> URL: https://issues.apache.org/jira/browse/SPARK-29232
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.4.0
> Reporter: Jiaqi Guo
> Priority: Critical
>
> We trained a RandomForestRegressionModel, and tried to access the trees. Even though the DecisionTreeRegressionModel is correctly built with the proper parameters from random forest, the parameter map is not updated, and still contains only the default value.
> For example, if a RandomForestRegressor was trained with maxDepth of 12, then accessing the tree information, extractParamMap still returns the default values, with maxDepth=5. Calling the depth itself of DecisionTreeRegressionModel returns the correct value of 12 though.
> This creates issues when we want to access each individual tree and build the trees back up for the random forest estimator.
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