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https://issues.apache.org/jira/browse/MADLIB-988?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan updated MADLIB-988:
-----------------------------------
Labels: gsoc2016 starter (was: gsoc starter)
> Model parameter weighting
> -------------------------
>
> Key: MADLIB-988
> URL: https://issues.apache.org/jira/browse/MADLIB-988
> Project: Apache MADlib
> Issue Type: New Feature
> Reporter: Frank McQuillan
> Labels: gsoc2016, starter
> Attachments: Model_parameter_weighting.pdf
>
>
> Summary
> There are several instances where assigning weights to training samples or
> observations is desirable in order to portray known information about the
> data or handle situations where data quality varies. For example, the
> training sample may have a disproportionate number of observations in certain
> classes, or the data may have been collected in a stratified manner with one
> strata having greater or lesser sampling intensity. In such cases,
> observations can be weighted to reflect the importance of each point in the
> fitted model.
> References
> [1] See requirements document authored by Pivotal data science team
> (attached)
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