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What is advice of when to use one-vs-all logit or multinomial logit regressions? Most importantly, which one has a higher prediction power? Can one test hypothesis and estimate confidence intervals in one-vs-all approach?

I came from economics and I have just recently started to dive into Machine Learning. I noticed that they use different approaches to estimate discrete choice models. However, I could not find any paper or website where somebody would compare those two approaches. I found this, but it does not answer the question of prediction power: Multinomial logistic regression vs one-vs-rest binary logistic regression

kjetil b halvorsen
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dart_kaide
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    We know they are two different models, why should we compare the prediction power? – SmallChess Apr 22 '17 at 12:53
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    Because we still want to predict the same thing: what option will be chosen. Therefore, you could compare, for example, how many right predictions were made on a new set of data using those two approaches. – dart_kaide Apr 22 '17 at 13:06
  • To me, they are **not** predicting the same thing. It's just Apple and Orange. – SmallChess Apr 23 '17 at 11:19

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