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https://issues.apache.org/jira/browse/SPARK-3383?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16240284#comment-16240284
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Yan Facai (颜发才) edited comment on SPARK-3383 at 11/6/17 1:28 PM:
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[~WeichenXu123] Good work! I'd like to take a look if time allows. Anyway, I
believe that unordered features can benefit a lot from your work.
was (Author: facai):
[~WeichenXu123] Good work! I'd like to take a look if time allows. Anyway, I
believe that unordered features can benefit a lot from the PR.
> DecisionTree aggregate size could be smaller
> --------------------------------------------
>
> Key: SPARK-3383
> URL: https://issues.apache.org/jira/browse/SPARK-3383
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> Storage and communication optimization:
> DecisionTree aggregate statistics could store less data (described below).
> The savings would be significant for datasets with many low-arity categorical
> features (binary features, or unordered categorical features). Savings would
> be negligible for continuous features.
> DecisionTree stores a vector sufficient statistics for each (node, feature,
> bin). We could store 1 fewer bin per (node, feature): For a given (node,
> feature), if we store these vectors for all but the last bin, and also store
> the total statistics for each node, then we could compute the statistics for
> the last bin. For binary and unordered categorical features, this would cut
> in half the number of bins to store and communicate.
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