Timeline for Ridge and Lasso in GLMs
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 17, 2021 at 8:39 | vote | accept | Claudio Moneo | ||
| May 23, 2021 at 16:00 | answer | added | EdM | timeline score: 1 | |
| May 23, 2021 at 9:59 | comment | added | Henry | Regularisation tends to shrink coefficients towards $0$ by penalising their magnitude in some way, and the greater the penalty is, the more this tends to happen. This is a general effect. But even with ordinary use you may see what look like exceptions: for example with lasso you can have a coefficient which disappears for some value of $\lambda$, reappears for a larger $\lambda$ and then disappears for an even larger value. stats.stackexchange.com/questions/154825/… illustrates an example | |
| May 23, 2021 at 9:36 | history | asked | Claudio Moneo | CC BY-SA 4.0 |