Timeline for When to scale or standardize data in regression [duplicate]
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Sep 8, 2017 at 11:52 | history | closed |
kjetil b halvorsen♦ mdewey Peter Flom regression Users with the regression badge or a synonym can single-handedly close regression questions as duplicates and reopen them as needed. |
Duplicate of What algorithms need feature scaling, beside from SVM? | |
| Sep 8, 2017 at 9:58 | review | Close votes | |||
| Sep 8, 2017 at 11:53 | |||||
| Jun 30, 2016 at 13:45 | history | tweeted | twitter.com/StackStats/status/748512873425571845 | ||
| Jun 30, 2016 at 9:19 | comment | added | prashanth | Data standardization is needed when there is distance computation, such as Euclidean, involved between the observations, or there is product of X and X_transpose computation. For instances, ridge regression needs the data to be normalized (or standardized) which involves product of X and X_transpose computation. Most common approaches for standardization are mean-std standardization and min-max standardization. Categorical variables can be replaced via on-hot encoding. stats.stackexchange.com/questions/186031/… | |
| May 1, 2016 at 3:28 | history | edited | Wis | CC BY-SA 3.0 |
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| May 1, 2016 at 3:08 | history | asked | Wis | CC BY-SA 3.0 |