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@NoMad It's mathematically impossible to prove A <=> B without measuring all intersections between A, B, Aᶜ, and Bᶜ (some of these measurements might be infinite, but only if their complement is measurably finite).
Even if you have high total accuracy, you can always have a significantly unspecific, unsensitive, or imprecise results. -
@hitko 🤔 I think you need to read your last sentence again, and look up how "accuracy" is actually calculated. you can't have "significance" without having some measure of distance for this "significance". Ans what you're saying is not relevant to error type 2. Type 2 does not discuss significance at all as far as I know.
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Actually, I just got super confused with the whole thing. Let's scrap this and start over. You're saying you want the type 2 error reported, which is false negatives. While accuracy is (tp+tn)/total. Which by default has the fn in total. But you don't want that. You want the some "distance" of these samples to be reported.
Am I getting that right so far? -
@NoMad (tp+tn)/total doesn't give the whole picture. Consider the following:
(5+100)/110 = 0.954, fp+fn = 5
Now, you might have fn = 5, fp = 0 in which case you only identified 50% positive cases, or you might have fn = 0, fp = 5 in which case there's 50% chance someone you identified as positive is actually negative, which can be equally worthless. And yet, in both cases you have 95.4% accuracy.
Most helpful error log ever...
Report to who?
When you get a little confused
We don't talk enough about type 2 error! So many papers everywhere are just pure trash because they don't account for it, and people are so fucking oblivious about it, they don't even catch the obvious ones. Even researchers and publications which are supposed to properly review their articles simply fail to ask the obvious "Did you measure the segment which doesn't fit either of your variables?"
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type 2 error
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