[jira] [Commented] (SPARK-14043) Remove restriction on maxDepth for decision trees

["Gaetan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-14043?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16726544#comment-16726544 ] 

Gaetan commented on SPARK-14043:
--------------------------------

In my opinion this is a mistake to not invest in machine learning improvements. Random forest is a very efficient algorithm for statisticians, often more than some logistic or polynomial regressions. This limit of depth is inconvenient from 1 billion of observations, so that we are forced to use another technology, beacause the error rate is too important.

> Remove restriction on maxDepth for decision trees
> -------------------------------------------------
>
>                 Key: SPARK-14043
>                 URL: https://issues.apache.org/jira/browse/SPARK-14043
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: Joseph K. Bradley
>            Priority: Minor
>
> We currently restrict decision trees (DecisionTree, GBT, RandomForest) to be of maxDepth <= 30.  We should remove this restriction to support deep (imbalanced) trees.
> Trees store an index for each node, where each index corresponds to a unique position in a binary tree.  (I.e., the first index of row 0 is 1, the first of row 1 is 2, the first of row 2 is 4, etc., IIRC)
> With some careful thought, we could probably avoid using indices altogether.



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