Timeline for Multinomial logistic regression vs one-vs-rest binary logistic regression
Current License: CC BY-SA 3.0
14 events
| when toggle format | what | by | license | comment | |
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| Nov 12, 2020 at 17:24 | answer | added | Gene M | timeline score: 3 | |
| Apr 6, 2020 at 19:27 | answer | added | seanv507 | timeline score: 6 | |
| Feb 16, 2019 at 16:42 | comment | added | Ben Ogorek | To reader: I recommend starting at @julieth's answer and following up by reading ttnphns'. I think the former more directly answers the original question but the latter adds some interesting context. ttnphns also shows the different features that are available for both in a popular software routine, which could itself constitute a reason to use one over the other (see gregmacfarlane's statement). | |
| Apr 7, 2017 at 9:04 | vote | accept | Tomek Tarczynski | ||
| S Oct 25, 2015 at 16:57 | history | suggested | Franck Dernoncourt | CC BY-SA 3.0 |
add one-vs-rest scheme terminology
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| Oct 25, 2015 at 16:05 | review | Suggested edits | |||
| S Oct 25, 2015 at 16:57 | |||||
| Jun 18, 2014 at 12:20 | comment | added | user48573 | Multinomial Logistic regression is the extension of binary logit regression. It is used when the dependent variables of the study is three and above, whereas, binary logit is used when the dependent variables of the study is two. | |
| Mar 14, 2013 at 14:16 | comment | added | Tomek Tarczynski | @gmacfarlane: I've tried to simulate data where MNL would be better than series of binary logistic regressions, but every time on average the quality was the same. I was comparing lift charts and after averaging results from few simulations they looke almost the same. Maybe You have an idea how to generate data so MNL beats binary logistic regressions? Although MNL had a great advantage, its scores could be interpreted as probability. | |
| Mar 14, 2013 at 11:52 | comment | added | gregmacfarlane | Mathematically, a multinomial logit model is a set of binary logit models, all compared against a base alternative. But because you get to collapse generic parameters and maybe combine some others, the MNL will always be at least as efficient (and probably more so). I see no reason to ever use a series of binomial models. | |
| Mar 13, 2013 at 16:01 | answer | added | ttnphns | timeline score: 35 | |
| Mar 13, 2013 at 15:12 | history | edited | Tomek Tarczynski | CC BY-SA 3.0 |
added 3 characters in body
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| Mar 13, 2013 at 15:08 | history | edited | user88 | CC BY-SA 3.0 |
edited title
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| Mar 13, 2013 at 15:07 | answer | added | julieth | timeline score: 23 | |
| Mar 13, 2013 at 14:31 | history | asked | Tomek Tarczynski | CC BY-SA 3.0 |