Timeline for How to choose a predictive model after k-fold cross-validation?
Current License: CC BY-SA 3.0
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| Jul 12 at 17:14 | comment | converted from answer | L Z | Just a follow up question, what exactly does "whole-data" mean? I saw everyone was using this term. Does it mean, after picking the model, I do not need to split data into train and validation. I can just use the all of the data as train to train the model? | |
| Jul 23, 2023 at 22:11 | comment | added | Ggjj11 | Do you know about a mathematical proof that training on the combined dataset is fine? In 1951 no proof was available doi.org/10.1177/001316445101100101 | |
| Jun 22, 2023 at 19:05 | comment | added | skan | And I guess you should check if the parameters of the new model are similar to the parameters of the CV model. | |
| Jan 14, 2018 at 1:12 | history | edited | Patrick Ng | CC BY-SA 3.0 |
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| Jan 13, 2018 at 14:34 | history | edited | Patrick Ng | CC BY-SA 3.0 |
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| Jan 13, 2018 at 13:47 | history | edited | Patrick Ng | CC BY-SA 3.0 |
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| Jan 13, 2018 at 8:20 | history | edited | Patrick Ng | CC BY-SA 3.0 |
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| Jan 13, 2018 at 7:14 | review | Late answers | |||
| Jan 13, 2018 at 8:54 | |||||
| Jan 13, 2018 at 6:56 | history | answered | Patrick Ng | CC BY-SA 3.0 |