Timeline for How to calculate out of sample R squared?
Current License: CC BY-SA 4.0
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| Sep 18 at 11:14 | history | edited | Knarpie | CC BY-SA 4.0 |
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| Jun 11, 2023 at 13:28 | history | edited | Dave | CC BY-SA 4.0 |
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| May 30, 2023 at 23:43 | comment | added | Dave | That is my point: that "other" term is not zero in general, and then claims of $R^2$ as being the proportion of variance explained become dubious. | |
| May 30, 2023 at 14:13 | comment | added | Knarpie | @Dave thanks for your kind words! In your derivation, if $y_i$ is the out-of-sample observation and $\bar{y}$ and $\hat{y}_i$ are calculated based on in-sample data (and out-of-sample regressors), I am not sure that your "Other" term equals zero in the general case of a random design. The proofs I find are for in-sample linear regression, which is the subject of your post, but aren't matters different for out-of-sample prediction? If the out-of-sample design is fixed, I think this term drops for any unbiased prediction model, not just for linear models. | |
| May 26, 2023 at 10:16 | comment | added | Dave | This is awesome. I disagree with the interpretation of explained variance, but this is a minor point. Your article makes the exact kind of argument I make all the time on here. It is good to see that in a legitimate journal. If I ever get around to writing an article I have in mind, I will cite your work. | |
| May 26, 2023 at 8:45 | history | edited | Firebug | CC BY-SA 4.0 |
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| May 26, 2023 at 8:41 | history | answered | Knarpie | CC BY-SA 4.0 |