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https://issues.apache.org/jira/browse/SPARK-3165?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15935646#comment-15935646
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Facai Yan edited comment on SPARK-3165 at 3/22/17 1:57 AM:
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Do you mean that:
TreePoint.binnedFeatures is Array[int], which doesn't use sparsity in data?
So those modifications is need:
1. modify TreePoint.binnedFeatures to Vector.
2. modify LearningNode.predictImpl method if need.
3. modify the methods about Bin-wise computation, such as binSeqOp, to
accelerate computation.
Please correct me if misunderstand.
I'd like to work on this if no one else has started it.
was (Author: facai):
Do you mean that:
TreePoint.binnedFeatures is Array[int], which doesn't sparsity in data?
So those modifications is need:
1. modify TreePoint.binnedFeatures to Vector.
2. modify LearningNode.predictImpl method if need.
3. modify the methods about Bin-wise computation, such as binSeqOp, to
accelerate computation.
Please correct me if misunderstand.
I'd like to work on it.
> DecisionTree does not use sparsity in data
> ------------------------------------------
>
> Key: SPARK-3165
> URL: https://issues.apache.org/jira/browse/SPARK-3165
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> Improvement: computation
> DecisionTree should take advantage of sparse feature vectors. Aggregation
> over training data could handle the empty/zero-valued data elements more
> efficiently.
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