Timeline for how to deal with two high correlations feature which both has a low correlation with target
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 24, 2019 at 13:36 | vote | accept | Bowen Peng | ||
| May 24, 2019 at 12:08 | answer | added | asmgx | timeline score: 1 | |
| May 16, 2019 at 2:24 | comment | added | aranglol | ...to using zero information from a variable), why not try an algorithm that controls for colinearity? Algorithms like ridge regression/partial least squares, support vector machines, tree based learners, etc. | |
| May 16, 2019 at 2:22 | comment | added | aranglol | Are you trying to do statistical inference or are you purely looking for improving predictive performance? If it is the latter, I question why you seem to care so much about correlation between features. Correlation can affect the stability of your model (leading to large fitted coefficients and high variance predictions) if you are using a typical linear regression (glm). However, most other algorithms are fairly indifferent to collinearity between variables. Instead of removing variables based off some arbitrary threshold (and therefore, also committing yourself... | |
| May 16, 2019 at 1:50 | history | asked | Bowen Peng | CC BY-SA 4.0 |