Timeline for When is oversampling poor practice?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Jun 19, 2022 at 9:07 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Mar 30, 2022 at 21:07 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Feb 26, 2022 at 19:08 | answer | added | Dave | timeline score: 3 | |
| Jan 14, 2022 at 20:22 | comment | added | Dave | If you already have the entire population, why do you need a model at all? You have some covariates; either the event happened for that combination of covariates or it did not. If you have multiple instances if the covariate combinations with different outcomes, you have that the outcome happened a certain proportion of the time and didn't happen some proportion of the time. | |
| Nov 6, 2020 at 16:38 | comment | added | kjetil b halvorsen♦ | What do you think oversampling can do that cannot be done with weights? Also see Frank Harrell's answer at stats.stackexchange.com/questions/199230/… | |
| Aug 15, 2017 at 8:35 | comment | added | Krrr | My take on this is that it depends on what you expect in the test sample. If you expect it to be imbalanced in a similar way then you can just use the appropriately threshold-ed probabilities, e.g. from random forest. If you want the trees to be more balanced (because you want the default threshold of 0.5 to be meaningful) then oversampling might help. You can also look at more sophisticated methods like the SMOTE. | |
| Aug 15, 2017 at 0:03 | history | tweeted | twitter.com/StackStats/status/897247403610439680 | ||
| Aug 14, 2017 at 20:04 | comment | added | boot-scootin | @MatthewDrury I take it you're not much a proponent of oversampling, then. My n is ~500,000, so it's not like I'm hurting for an absolute number of the positive class (with about 3,000 events). I'm thinking I'll just forget about oversampling in this case. | |
| Aug 14, 2017 at 18:54 | comment | added | Matthew Drury | I've never got a clear answer on when it is a good practice: stats.stackexchange.com/questions/285231/… . There is the king paper, but the general oversampling methods don't seem to address the issues discussed there. Until otherwise, I'm inclined to think these methods are over-discussed nonsense. I would love to be corrected though. | |
| Aug 14, 2017 at 18:34 | history | asked | boot-scootin | CC BY-SA 3.0 |