Assume original data contains 1000 goods and 1 bad I build a logistic regression and use the the model to score the bad and I get probability = 0.00001 Then I use oversampling/undersampling to increase/decrease the original data so now I have 1000 goods and 1000 bags if I use oversampling. Then I build a logistic model use the data and apply the model to the original data then for that bad I get probability = 0.5. However this probability need to be adjusted to reflect original data so after doing some math you get adjusted probability lower than 0.5 (for example 0.00001 ) so what is the point of oversampling/undersampling if you are required to adjust the probability?
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4The point of oversampling is unclear to many. See the discussion in the comments to Are unbalanced datasets problematic, and (how) does oversampling (purport to) help? – Stephan Kolassa Jul 07 '21 at 15:18
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Still don’t understand – gyambqt Jul 07 '21 at 22:39
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1It would help to explain what it is you do not understand. – Dave Jul 07 '21 at 23:54
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My question is asking why do you need to adjust probability after – gyambqt Jul 08 '21 at 11:19
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3I think @StephanKolassa will agree with me that you do not need to do that, because you should not be oversampling or undersampling in the first place. I find it hard to argue for a correct conclusion to a wrong procedure. – Dave Jul 08 '21 at 11:51