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I have a highly unbalanced binary dependent variable (i.e. cases of '1' is <5%). I am trying to implement SMOTE algorithm using R DMwR package. I wonder in general, how we determine the parameters such as perc.over and perc.under indicating how much we need to oversample or undersample the minority or majority class respectively.

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Create a loop so that you can loop through different values of the percentage and see which gives you the best accuracy or f-score. ie 100%, 200% , ... for perc.over. For perc.under you can maj to min ratio multiplied by the inital oversampling percenatge.

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  • $\begingroup$ can you explain a bit more clearly? Please avoid abbreviations and shorthand. $\endgroup$
    – Glen_b
    Aug 29, 2016 at 8:03

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