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@glemaitre glemaitre commented Jul 31, 2016

It is the missing part from PR #115

@dvro I forgot something at that time. How shall we handle if the dataset is multiclass? Do we consider to reduce only the number of samples in the minority class?

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coveralls commented Jul 31, 2016

Coverage Status

Coverage increased (+0.002%) to 98.046% when pulling cd8c1be on glemaitre:make_imbalance into 84e6af4 on scikit-learn-contrib:master.

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dvro commented Jul 31, 2016

@glemaitre at this time, yes. After all, is the proportion of "positive" and "non-positive" ("negative" for binary datasets) that determines the imbalance ratio.

But, for complex multi-class transformation, we could make a make_multiclass_imbalance in which one would pass the desired proportion of each class through a dict.

At least, this is how I think it should be.

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@dvro ok with me

@glemaitre glemaitre changed the title [WIP] Make imbalance [MRG] Make imbalance Jul 31, 2016
@glemaitre glemaitre merged commit 19969f6 into scikit-learn-contrib:master Jul 31, 2016
christophe-rannou pushed a commit to christophe-rannou/imbalanced-learn that referenced this pull request Apr 3, 2017
* PEP8 and doc for make_imbalance

* Add logger for the module
glemaitre added a commit to glemaitre/imbalanced-learn that referenced this pull request Jun 15, 2017
* PEP8 and doc for make_imbalance

* Add logger for the module
glemaitre added a commit to glemaitre/imbalanced-learn that referenced this pull request Jun 15, 2017
* PEP8 and doc for make_imbalance

* Add logger for the module
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3 participants