Timeline for Geometric interpretation of penalized linear regression
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| Nov 4, 2022 at 15:04 | answer | added | Sextus Empiricus | timeline score: 1 | |
| S Jan 26, 2017 at 12:20 | history | suggested | CommunityBot | CC BY-SA 3.0 |
more clear wording
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| Jan 26, 2017 at 11:31 | review | Suggested edits | |||
| S Jan 26, 2017 at 12:20 | |||||
| Jan 20, 2014 at 9:54 | comment | added | denis | "the line vertically closest to all the points" ? One usually takes the sum of squares -- see the nice picture on Wikipedia Coefficient_of_determination. The sum of vertical distances is the L1 norm, which is less sensitive to outliers but much less common. | |
| Jun 21, 2012 at 17:22 | vote | accept | Lucas Reis | ||
| Jun 18, 2012 at 22:08 | answer | added | JohnRos | timeline score: 3 | |
| Jun 15, 2012 at 14:21 | answer | added | Dmitry Laptev | timeline score: 32 | |
| Jun 14, 2012 at 16:34 | history | tweeted | twitter.com/#!/StackStats/status/213308400291950592 | ||
| Jun 14, 2012 at 16:32 | history | edited | Lucas Reis | CC BY-SA 3.0 |
added 178 characters in body
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| Jun 14, 2012 at 16:02 | comment | added | Dmitry Laptev | I am not sure that it is possible to come up with such an interpretation. Simply because what you provided are images in the original space of features and responses. And penalized regression involves the space of coefficients, which is very different. | |
| Jun 14, 2012 at 15:05 | history | asked | Lucas Reis | CC BY-SA 3.0 |