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https://issues.apache.org/jira/browse/SPARK-10788?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14936219#comment-14936219
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Seth Hendrickson commented on SPARK-10788:
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[~josephkb] I'm interested in working on this issue, but I'm not sure I see the
problem. Looking through ML RandomForest implementation I found that
{{numBins}} for unordered features is {{def numUnorderedBins(arity: Int): Int =
2 * ((1 << arity - 1) - 1)}} and that {{numSplits}} is just {{numBins / 2}}.
In the 3 category example: {{numBins = 2 * (( 1 << (3 - 1)) - 1) = 6}} and so
the number of splits considered is {{numSplits = 6 / 2 = 3}}. This seems to be
the same as in the MLlib implementation. Perhaps I am overlooking something.
I'd appreciate any feedback...
> Decision Tree duplicates bins for unordered categorical features
> ----------------------------------------------------------------
>
> Key: SPARK-10788
> URL: https://issues.apache.org/jira/browse/SPARK-10788
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Joseph K. Bradley
>
> Decision trees in spark.ml (RandomForest.scala) effectively creates a second
> copy of each split. E.g., if there are 3 categories A, B, C, then we should
> consider 3 splits:
> * A vs. B, C
> * A, B vs. C
> * A, C vs. B
> Currently, we also consider the 3 flipped splits:
> * B,C vs. A
> * C vs. A, B
> * B vs. A, C
> This means we communicate twice as much data as needed for these features.
> We should eliminate these duplicate splits within the spark.ml implementation
> since the spark.mllib implementation will be removed before long (and will
> instead call into spark.ml).
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