[
https://issues.apache.org/jira/browse/MADLIB-1095?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-1095:
------------------------------------
Fix Version/s: (was: v1.12)
v1.11
> 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: Improvement
> Components: Module: Decision Tree
> Reporter: Frank McQuillan
> 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
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
