ASF GitHub Bot commented on MADLIB-1168:

Github user asfgit closed the pull request at:


> Balance datasets
> ----------------
>                 Key: MADLIB-1168
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1168
>             Project: Apache MADlib
>          Issue Type: New Feature
>          Components: Module: Sampling
>            Reporter: Frank McQuillan
>            Assignee: ssoni
>            Priority: Major
>             Fix For: v1.14
>         Attachments: MADlib Balance Datasets Requirements.pdf, 
> MADlib_Balance_Datasets_Requirements_v2.pdf
> From [1] here is the motivation behind balancing datasets:
> “Most classification algorithms will only perform optimally when the number 
> of samples of each class is roughly the same. Highly skewed datasets, where 
> the minority is heavily outnumbered by one or more classes, have proven to be 
> a challenge while at the same time becoming more and more common.
> One way of addressing this issue is by re-sampling the dataset as to offset 
> this imbalance with the hope of arriving at a more robust and fair decision 
> boundary than you would otherwise.
> Re-sampling techniques can be divided in these categories:
> * Under-sampling the majority class(es).
> * Over-sampling the minority class.
> * Combining over- and under-sampling.
> * Create ensemble balanced sets.”
> There is an extensive literature on balancing datasets.  The plan for MADlib 
> in the initial phase is to offer basic functionality that can be extended in 
> later phases based on feedback from users.  
> Please see attached document for proposed scope of this story.
> References
> [1] imbalance-learn Python project
> http://contrib.scikit-learn.org/imbalanced-learn/stable/index.html
> https://github.com/scikit-learn-contrib/imbalanced-learn

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