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https://issues.apache.org/jira/browse/MADLIB-1095?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15985787#comment-15985787
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ASF GitHub Bot commented on MADLIB-1095:
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Github user asfgit closed the pull request at:
https://github.com/apache/incubator-madlib/pull/125
> Use populated parts of feature vector even if it contains one or more NULL
> entries
> ----------------------------------------------------------------------------------
>
> Key: MADLIB-1095
> URL: https://issues.apache.org/jira/browse/MADLIB-1095
> Project: Apache MADlib
> Issue Type: Bug
> Components: Module: Decision Tree
> Reporter: Frank McQuillan
> Priority: Minor
> Fix For: v1.11
>
>
> Context
> Currently in DT/RF if the feature vector contains any NULLs, the whole row
> will be ignored in the training data. This is not ideal, especially in the
> case where training data is sparse.
> Story
> As a data scientist, I want the DT/RF modules to use the non-NULL parts of
> the feature vector, and not discard the whole row, so that I can get better
> accuracy for classification/regression in the case of sparse data.
> Acceptance
> TBD
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