Timeline for Cross-validation and building a final model when using hyperparameter optimization
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Dec 13, 2018 at 11:48 | comment | added | amoeba | @RomainReboulleau "my cross-validation estimation of my model performance is slightly biaised (so may be too optimistic regarding the user criterion), but in the end, the "best" model is the one from option #1" -- yes, that's correct. | |
| Dec 13, 2018 at 11:16 | comment | added | Romain Reboulleau | Thank you for this very clear answer. If I understand well: my cross-validation estimation of my model performance is slightly biaised (so may be too optimistic regarding the user criterion), but in the end, the "best" model is the one from option #1. I will also have a look at nested cross-validation to get a "benchmark" method of the hyper-parameter optimization. | |
| Dec 13, 2018 at 11:07 | history | bounty ended | Romain Reboulleau | ||
| Dec 13, 2018 at 11:07 | vote | accept | Romain Reboulleau | ||
| Dec 13, 2018 at 8:18 | history | edited | amoeba | CC BY-SA 4.0 |
added 39 characters in body
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| Dec 13, 2018 at 0:07 | comment | added | usεr11852 | +1. Sometimes I like to think of a model-building procedure as a ranking process. I rank possible models to find the best one. I care for the order rather than the magnitude of the performance index. | |
| Dec 12, 2018 at 23:58 | history | answered | amoeba | CC BY-SA 4.0 |