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https://issues.apache.org/jira/browse/SPARK-3383?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15963741#comment-15963741
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Yan Facai (颜发才) edited comment on SPARK-3383 at 4/12/17 2:02 AM:
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I think the task contains two subtask:
1. separate `split` with `bin`: Now for each categorical feature, there is 1
bin per split. That's said, for N categories, the communicate cost is 2^(N-1) -
1 bins. However, if we only get stats for each category, and construct splits
finally. Namely, 1 bin per category. The communicate cost is N bins.
2. As said in Description, store all but the last bin, and also store the total
statistics for each node. The communicate cost will be N-1 bins.
I have a question:
1. why unordered features only are allowed in multiclass classification?
was (Author: facai):
I think the task contains two subtask:
1. separate `split` with `bin`: Now for each categorical feature, there is 1
bin per split. That's said, for N categories, the communicate cost is 2^{N-1} -
1 bins. However, if we only get stats for each category, and construct splits
finally. Namely, 1 bin per category. The communicate cost is N bins.
2. As said in Description, store all but the last bin, and also store the total
statistics for each node. The communicate cost will be N-1 bins.
I have a question:
1. why unordered features only are allowed in multiclass classification?
> 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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