Timeline for feature redundancy
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Jun 18, 2016 at 4:55 | history | edited | Hack-R | CC BY-SA 3.0 |
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| Jun 18, 2016 at 4:54 | comment | added | Hack-R | @ArmonSafai On a related note datascience.stackexchange.com/questions/12250/… | |
| Jun 18, 2016 at 4:52 | comment | added | Hack-R | @ArmonSafai The way you use PCA for feature selection is to look at the factor loading on the principal components and determine which correlated variables are measuring the same principal component then pick the top 1 or few variables to represent that latent variable, eliminating highly correlated non-distinct features | |
| Jun 18, 2016 at 4:45 | comment | added | Armon Safai | But i was just informed by others that PCA doesnt eliminate redundant features | |
| Jun 18, 2016 at 2:20 | history | edited | Hack-R | CC BY-SA 3.0 |
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| Jun 18, 2016 at 2:01 | history | answered | Hack-R | CC BY-SA 3.0 |