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Posted to issues@spark.apache.org by "Seth Hendrickson (JIRA)" <ji...@apache.org> on 2015/10/01 22:05:26 UTC
[jira] [Comment Edited] (SPARK-10788) Decision Tree duplicates bins
for unordered categorical features
[ https://issues.apache.org/jira/browse/SPARK-10788?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14940316#comment-14940316 ]
Seth Hendrickson edited comment on SPARK-10788 at 10/1/15 8:04 PM:
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Yes, much clearer, thanks! I can work on this task.
was (Author: sethah):
Yes, much clearer. I can work on this task.
> 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) communicate twice as much data as needed for unordered categorical features. Here's an example.
> Say there are 3 categories A, B, C. We consider 3 splits:
> * A vs. B, C
> * A, B vs. C
> * A, C vs. B
> Currently, we collect statistics for each of the 6 subsets of categories (3 * 2 = 6). However, we could instead collect statistics for the 3 subsets on the left-hand side of the 3 possible splits: A and A,B and A,C. If we also have stats for the entire node, then we can compute the stats for the 3 subsets on the right-hand side of the splits. In pseudomath: {{stats(B,C) = stats(A,B,C) - stats(A)}}.
> We should eliminate these extra bins 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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