Timeline for Is it valid to implement hyper-parameter tuning and THEN cross-validation?
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Dec 8, 2021 at 11:02 | comment | added | user9317212 | great! thank you! no - it is not, but i wanted to use the most simple example just be concrete about my point - | |
| Dec 8, 2021 at 10:57 | comment | added | nbro | @user9317212 Yes, I would recommend that you remove the validation data before doing the k-fold CV. However, if your dataset is really that small, I don't know if it even makes sense to perform HP optimization with 1 sample. | |
| Dec 8, 2021 at 10:54 | comment | added | user9317212 | thank you! this answers my question. so would you recommend just throwing away the validation data used for hyperparameter tuning during k-fold cross val? so for example, if my training set for HP tuning is 10 total samples with 1 sample used for validation, would you simply remove that one sample for k-fold and train on 9 samples? | |
| Dec 8, 2021 at 10:51 | vote | accept | user9317212 | ||
| Dec 8, 2021 at 10:45 | history | answered | nbro | CC BY-SA 4.0 |