I'm building a logistic regression model with a binary target. My target has a ratio of 4:1 (i.e., it has 4 times as many values for one class than the other).

Is there anything I have to consider with this information? I have seen this question, however I'm not satisfied with the first answer, and then the second answer just says that "there may not be sufficient patterns belonging to the minority class to adequately represent its distribution".

So, is there anything else to consider? Is there anything connected to the sensitivity or specificity that may arise as a problem or even anything else?


1 Answer 1


Unbalanced classes are almost certainly not a problem, and oversampling will not solve a non-problem: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?

Do not use accuracy to evaluate a classifier: Why is accuracy not the best measure for assessing classification models? Is accuracy an improper scoring rule in a binary classification setting? Classification probability threshold The same problems apply to sensitivity and specificity, and indeed to all evaluation metrics that rely on hard classifications. Instead, use probabilistic classifications, and evaluate these using proper scoring rules.


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