Timeline for Confusion regarding K-fold Cross Validation
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 21, 2022 at 8:14 | vote | accept | AAA | ||
| Nov 20, 2022 at 17:02 | comment | added | gunes | That is correct. In the second one, an alternative method (as described above) is to get all the predictions for the entire dataset and calculate the performance (especially if fold sizes are small). | |
| Nov 20, 2022 at 16:55 | comment | added | AAA | Thanks alot. So to make sure that I have understand you correctly. If my target is to optimize hyperparameters, then I train using k fold cross validation and the train dataset while I am finding the best parameters, then when I find them, I train the model again using the best parameters and then evaluate using the test set. On the other hand, If I need to evaluate the model only, then I use the whole dataset as k-fold cross validation in this case will do the splitting internally and the final performance is the average performance of all the iterations. Is that correct ? | |
| Nov 20, 2022 at 16:24 | history | answered | gunes | CC BY-SA 4.0 |