Timeline for Is this a proper way to perform cross validation?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Jun 29, 2020 at 20:04 | comment | added | gunes | Here, by increasing the variance, I mean increasing the possibility of overfitting. Yes, you can k-fold on feature subsets. | |
| Jun 29, 2020 at 20:04 | comment | added | user5965026 | "The procedure that the student employs is actually choosing a dataset." Hmm this seems to be exactly what he did under the hood. He found a training dataset and testing dataset that gave a $\hat{\beta}$ that achieves the best prediction error. That doesn't seem to serve as practical use. | |
| Jun 29, 2020 at 20:02 | comment | added | user5965026 | Would you ever use CV for plain OLS? I think in a way, you can view the number of explanatory variables / features in OLS as hyperparameters, and use CV for feature selection of a subset of the original features? When you say "increases the variance," what are you saying increases in variance? There seems to be some confusion on this based on other posts on k-Fold Validation that I've read. But those seem to be focused on variance increasing in the prediction error as $k$ increases. | |
| Jun 29, 2020 at 19:51 | history | answered | gunes | CC BY-SA 4.0 |