Timeline for Best way to bin continuous data
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Aug 18, 2015 at 18:23 | vote | accept | Jamie Dixon | ||
| Aug 19, 2015 at 1:51 | |||||
| Aug 13, 2015 at 1:11 | comment | added | gung - Reinstate Monica | @AndyW, they are similar but won't be universally equivalent. LDA assumes age is normally distributed; if so, it will work slightly better--especially as the distributions get further apart. MLR can handle categorical variables (sex, race, etc) as well, so can be more generally applicable, but it seems to me a little more advanced to understand & use (although your example was very straightforward, so maybe not). MLR is a viable option; you could add it as an answer, if you wanted. | |
| Aug 13, 2015 at 1:06 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
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| Aug 11, 2015 at 13:10 | comment | added | Andy W | Neat idea. I'm not sure if there is a universal equivalence, but making the class predictions via a multinomial model results in the same predictions for your Iris example. Example here. That has an example plot to show uncertainty in the predictions as well. | |
| Aug 11, 2015 at 3:16 | history | answered | gung - Reinstate Monica | CC BY-SA 3.0 |