You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@spark.apache.org by "zhengruifeng (Jira)" <ji...@apache.org> on 2020/01/17 07:48:00 UTC

[jira] [Created] (SPARK-30543) RandomForest add Param bootstrap to control sampling method

zhengruifeng created SPARK-30543:
------------------------------------

             Summary: RandomForest add Param bootstrap to control sampling method
                 Key: SPARK-30543
                 URL: https://issues.apache.org/jira/browse/SPARK-30543
             Project: Spark
          Issue Type: Bug
          Components: ML, PySpark
    Affects Versions: 3.0.0
            Reporter: zhengruifeng


Current RF with numTrees=1 will directly build a tree using the orignial dataset,

while with numTrees>1 it will use bootstrap samples to build trees.

This design is to train a DecisionTreeModel by the impl of RandomForest, however, it is somewhat strange.

In Scikit-Learn, there is a param bootstrap to control bootstrap samples are used.



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org