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https://issues.apache.org/jira/browse/MADLIB-1168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16298746#comment-16298746
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ssoni edited comment on MADLIB-1168 at 12/20/17 5:04 PM:
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[~fmcquillan] Question on the "class sizes" parameter, where either the desired
number of observations like ‘male=3000, female=7000’ or a fraction like
‘male=0.4, female=0.6’ is specified. In such cases, should the rest of the
classes in the sample be included in the output table or only the classes
mentioned in the class_sizes parameter?
was (Author: ssoni):
Question on the "class sizes" parameter, where either the desired number of
observations like ‘male=3000, female=7000’ or a fraction like ‘male=0.4,
female=0.6’ is specified. In such cases, should the rest of the classes in the
sample be included in the output table or only the classes mentioned in the
class_sizes parameter?
> 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
> Fix For: v2.0
>
> 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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