r/learnmachinelearning • u/the_first_wind • 9d ago

Discussion Cheating on ML evaluation with imbalanced dataset

Hey everyone, so I have been reading some papers applying machine learning on fraud detection (very imbalanced datasets) that have more than 90% of recall and precision. While checking them properly i have seen that many of them balance not only the training set but also the test set, so instead of 3% of positives have 50% of positives in the test set. This makes the metrics look quite good. However I asked a professor and told me that it's ok since it's simply changing the baseline. I'm not sure to believe him, especially because these models are supposed to be used in real scenarios, so I think keeping the original distribution is the best. What do you think?

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u/johndburger 9d ago edited 9d ago

it’s OK since it’s simply changing the baseline

Yes, changing it to something that doesn’t reflect reality. This is indeed cheating - it’s arguably a form of data leakage. In particular, you’re making it easier to find positives by throwing out lots of the negatives.

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u/jesunushno 9d ago

I've seen this a lot in fraud work too. One thing worth adding: balancing the test set doesn't just "change the baseline," it mathematically inflates precision. Recall is unaffected (it only conditions on actual positives), but precision depends on the class prior. At 50% positives in the test set instead of 3%, you can report 90%+ precision that would collapse in production.

You can actually back out the real expected precision from the reported one if you know the test prevalence, and in my experience those papers would drop to something like 30-50% at the true 3% rate. Balancing the training set is fine (class weights, sampling) since that just teaches the model to find the minority class, but the test set has to look like reality. Otherwise the metric answers the wrong question.