Timeline for K-Fold cross validation-How to calculate regular parameters/hyper-parameters of the algorithms
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Jul 30, 2021 at 2:15 | vote | accept | Mohsen Sichani | ||
| Jul 27, 2021 at 15:49 | history | edited | Mohsen Sichani | CC BY-SA 4.0 |
edited title
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| Jul 27, 2021 at 4:13 | history | became hot network question | |||
| Jul 27, 2021 at 2:21 | answer | added | Erwan | timeline score: 5 | |
| Jul 26, 2021 at 21:15 | comment | added | Mohsen Sichani | I am trying to understand how it works, So these are the hyperparameters of a single algorithm or each time a new algorithm will be executed? I cannot understand "then pock the best one according to the five out-of-sample performance calculations", what do you mean? does it pick the best hyperparameter among then k different execution? if yes, then why is the error the average of the K executions? | |
| Jul 26, 2021 at 20:53 | comment | added | Dave | What are you trying to do with the cross validation? A common use is to optimize hyperparameters, such as the penalty in regularized regression. The approach would be to try out many different regularization penalties on the $k$ folds, then pick the best one according to the five out-of-sample performance calculations. Then you go and fit on the whole data set (except maybe a final holdout sample). | |
| Jul 26, 2021 at 20:11 | review | First posts | |||
| Jul 27, 2021 at 4:56 | |||||
| Jul 26, 2021 at 20:10 | history | asked | Mohsen Sichani | CC BY-SA 4.0 |