Timeline for Generalized linear models: What's the benefit of the underlying theory?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Nov 12, 2020 at 16:16 | history | edited | kjetil b halvorsen | CC BY-SA 4.0 |
deleted 10 characters in body
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| Sep 15, 2020 at 22:48 | vote | accept | Manuel Huppertz | ||
| Sep 15, 2020 at 19:46 | comment | added | Paul Siegel | Relatedly: I read somewhere that among the thousands of machine learning models Google has in production, something like 80% of them are GLM's. They are kind of the unsung heros of the machine learning world. | |
| Sep 15, 2020 at 19:44 | comment | added | Paul Siegel | This is sort of implicit in @kjetilbhalvorsen's answer, so I'll leave it as a comment rather than an additional answer. A big part of the motivation comes from engineering: the numerical algorithms used to fit GLM's to data are pretty similar across the whole class of models, so you can decouple the hardcore software engineering from the downstream applications. As a practitioner, this is a big deal: any time I can express a model as a GLM I know I won't have to worry too much about running the model at scale. | |
| Sep 15, 2020 at 17:00 | history | became hot network question | |||
| Sep 15, 2020 at 16:11 | answer | added | kjetil b halvorsen | timeline score: 8 | |
| Sep 15, 2020 at 9:06 | history | edited | YCor | CC BY-SA 4.0 |
edited tags, added Wikipedia link
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| Sep 15, 2020 at 9:01 | review | First posts | |||
| Sep 15, 2020 at 9:38 | |||||
| Sep 15, 2020 at 8:59 | history | asked | Manuel Huppertz | CC BY-SA 4.0 |