Timeline for Do I need to run PCA over all predictors in a regression model? Can I run it only over the continuous ones?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 15, 2016 at 14:58 | history | tweeted | twitter.com/StackStats/status/731861265447026688 | ||
| May 12, 2016 at 23:10 | vote | accept | plumbus_bouquet | ||
| May 12, 2016 at 22:47 | answer | added | EdM | timeline score: 5 | |
| May 12, 2016 at 21:27 | comment | added | amoeba | @EdM (+1) Consider posting this as an answer. | |
| May 12, 2016 at 21:23 | comment | added | plumbus_bouquet | @EdM that's a really good point. I might do all of the above and and compare the CV score at the end. | |
| May 12, 2016 at 21:20 | comment | added | EdM | LASSO and ridge regression can also deal with categorical data as predictors when you need to reduce dimensionality. The dummy variables are just coded numeric 0/1 and can be standardized like the continuous variables if you want to adjust for scaling that way. Ridge regression is essentially principal-components regression, except that the components are weighted in a graded way rather than yes/no for inclusion in the final model. But at @amoeba put it, "Whether it's going to end up being useful, nobody can say in advance." | |
| May 12, 2016 at 21:01 | history | edited | amoeba | CC BY-SA 3.0 |
deleted 5 characters in body; edited tags; edited title
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| May 12, 2016 at 19:03 | comment | added | amoeba | You could do that, there is no problem with it. Whether it's going to end up being useful, nobody can say in advance. | |
| May 12, 2016 at 5:15 | review | First posts | |||
| May 12, 2016 at 5:22 | |||||
| May 12, 2016 at 5:14 | history | asked | plumbus_bouquet | CC BY-SA 3.0 |