likeamanyfacedgod

likeamanyfacedgod OP t1_it6ln21 wrote

you can even test it yourself by balancing and unbalancing your model.
Look at how it is calculated, the TPR and FPR are both fractions, so it
won't matter if one is a larger class than the other. What does matter
though is if you care more about predicting one class than the other.

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likeamanyfacedgod OP t1_it6gqnz wrote

It's not trivial to me at all. I've seen a few blog posts that make this statement, but from my own experience, it's not true, you can even test it yourself by balancing and unbalancing your model. Look at how it is calculated, the TPR and FPR are both fractions, so it won't matter if one is a larger class than the other. What does matter though is if you care more about predicting one class than the other.

−38

likeamanyfacedgod OP t1_is0nr3t wrote

well it's purely theoretical for now. Imagine that I create a tool that predicts something important with a high degree of accuracy. And this tool - whilst many other machine learning engineers could also make it if they took the required time - is not something you can make in a week after following a few "towardsdatasci" articles... And then imagine that other companies are interested in predicting that thing. It may be the case that a few other companies have/claim to have a similar tool that's equally as accurate. Those companies could either A) employ their own data science team to recreate the tool and hope that that team can achieve the same accuracy. Or B) Buy my tool and have something that's guaranteed to work. So my question is: does "B" from the above ever actually happen in practice?

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