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