Timeline for Do the benefits of ridge regression diminish with larger datasets?
Current License: CC BY-SA 4.0
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| Sep 21, 2021 at 17:07 | history | edited | Ethan | CC BY-SA 4.0 |
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| Sep 21, 2021 at 17:04 | history | rollback | Ethan |
Rollback to Revision 3
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| Sep 20, 2021 at 22:54 | history | rollback | Ethan |
Rollback to Revision 2
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| Sep 20, 2021 at 22:34 | history | edited | Shayan Shafiq | CC BY-SA 4.0 |
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| Dec 4, 2020 at 20:15 | vote | accept | Rocky the Owl | ||
| Dec 4, 2020 at 20:13 | comment | added | Rocky the Owl | Okay thank you very much! (I would upvote the post but I do not have enough reputation) | |
| Dec 4, 2020 at 20:08 | comment | added | Ethan | Consequently, it would be difficult to say whether ridge regression would or would not perform better in either of these cases without trying it both ways to see. | |
| Dec 4, 2020 at 20:07 | comment | added | Ethan | I think a generalization in this regard is somewhat difficult to draw as it will depend on the data that comprises each of the data sets. I would say that depends. If you have a data set or 50k intuition would suggest that we might get a better fit, however since OLS is at high risk of being effected by outliers perhaps if there are many present in that data set the fit will be worse. If the dataset of 1k however follows a linear pattern almost exactly the fit might be better. | |
| Dec 4, 2020 at 20:01 | comment | added | Rocky the Owl | Thanks for this response. When I said 'larger datasets', I was referring to number of data points rather than parameters. You are correct that OLS doesn't have feature selection. Generally speaking, for a dataset of size 50,000 (with ~10 parameters) would we still expect ridge regression to outperform as significantly as it might to with a dataset of size 1,000? Thanks for the help! | |
| Dec 4, 2020 at 18:58 | history | edited | Ethan | CC BY-SA 4.0 |
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| Dec 4, 2020 at 18:47 | history | answered | Ethan | CC BY-SA 4.0 |