[jira] [Comment Edited] (SPARK-3165) DecisionTree does not use sparsity in data

["Facai Yan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3165?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15935646#comment-15935646 ] 

Facai Yan edited comment on SPARK-3165 at 3/22/17 1:57 AM:
-----------------------------------------------------------

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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