Timeline for Using KNN for prediction, how should I normalize my data?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Mar 25, 2023 at 22:31 | history | edited | Firebug |
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| Sep 9, 2016 at 17:21 | answer | added | Haitao Du | timeline score: 0 | |
| Sep 9, 2016 at 17:16 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 4, 2016 at 0:40 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jul 4, 2016 at 20:18 | answer | added | Firebug | timeline score: 3 | |
| Nov 7, 2015 at 6:12 | history | tweeted | twitter.com/StackStats/status/662875187428376576 | ||
| Oct 3, 2013 at 12:26 | comment | added | ASOF | Well, that's a step away from simplicity and toward potential overfitting that I wouldn't want to take. | |
| Oct 3, 2013 at 11:46 | comment | added | zkurtz | A more important consideration might be how to scale each variable. Even if all the variables were continuous, I wouldn't necessarily normalize them all the same way -- if the association with the response variable is stronger for x1 than for x2, I'd want to keep the variance on x1 higher than for x2. For example, scale x1 as normal with mean 0 and variance 4, whereas x2 gets variance 1. | |
| Oct 3, 2013 at 11:31 | review | First posts | |||
| Oct 3, 2013 at 11:53 | |||||
| Oct 3, 2013 at 11:14 | history | asked | ASOF | CC BY-SA 3.0 |