Timeline for Overfitting in Linear Regression
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 28, 2020 at 17:22 | answer | added | Peteris | timeline score: 5 | |
| Aug 28, 2020 at 11:11 | comment | added | Nat | To be clear, your model's $f\left(x, y\right)= a x + b y + c ,$ but someone told you that it was overfitting? Some additional background/context may help, as that claim would seem to be odd. At least, assuming independence between $x$ and $y ;$ if one's a function of the other or something, then such a model could be argued as being an overfit. | |
| Aug 27, 2020 at 20:33 | comment | added | Dave | You don't even need to add polynomial features like @RobertLong did in order to badly overfit a linear model! datascience.stackexchange.com/a/79994/73930 | |
| Aug 27, 2020 at 17:38 | comment | added | oW_ | For two variables and a linear decision surface this will indeed not be much of a concern unless one or both variables are completely unrelated to the target. Underfitting is likely the bigger problem. (Just note that linear regression doesn't have to produce a linear decision surface, like polynomial (linear) regression, as shown in the other answers.) | |
| Aug 27, 2020 at 17:10 | answer | added | Dhanush kumar | timeline score: 4 | |
| Aug 27, 2020 at 16:55 | history | became hot network question | |||
| Aug 27, 2020 at 10:58 | vote | accept | Sachin Krishna | ||
| Aug 27, 2020 at 10:58 | |||||
| S Aug 27, 2020 at 10:48 | history | suggested | Robert Long |
added statistics tag
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| Aug 27, 2020 at 10:26 | review | Suggested edits | |||
| S Aug 27, 2020 at 10:48 | |||||
| Aug 27, 2020 at 10:18 | answer | added | Robert Long | timeline score: 26 | |
| Aug 27, 2020 at 9:23 | vote | accept | Sachin Krishna | ||
| Aug 27, 2020 at 10:53 | |||||
| Aug 27, 2020 at 8:52 | history | asked | Sachin Krishna | CC BY-SA 4.0 |