Timeline for How many ways are there to check model overfitting?
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
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| Oct 5, 2020 at 6:44 | history | edited | Itamar Mushkin | CC BY-SA 4.0 |
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| Jul 22, 2020 at 12:04 | vote | accept | DN1 | ||
| Jul 12, 2020 at 9:22 | comment | added | Itamar Mushkin | @DN1 I've added a specific answer to random forests. | |
| Jul 12, 2020 at 9:22 | history | edited | Itamar Mushkin | CC BY-SA 4.0 |
added 981 characters in body
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| Jul 7, 2020 at 13:22 | comment | added | Itamar Mushkin | You're welcome. If the answer helped your question, please "accept" it (with the little green 'v' sign) so others know it's closed. | |
| Jul 7, 2020 at 13:18 | comment | added | DN1 | Thank you and sorry about that, edited my question now with these details | |
| Jul 7, 2020 at 13:17 | history | edited | Itamar Mushkin | CC BY-SA 4.0 |
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| Jul 7, 2020 at 13:13 | comment | added | Itamar Mushkin | Separating the data to a training dataset and a testing dataset is exactly the process I referred to in my first comment to you... add this to your question, and I'll edit my answer accordingly | |
| Jul 7, 2020 at 13:05 | comment | added | DN1 | Thank you this is very helpful. The 0.88 was the mean across folds and rounding up. I run the nested CV on the entirety of the data so there is not training data, but when I separate the data to run a training dataset and testing dataset I get: Train r2: 0.971 Test r2: 0.868. | |
| Jul 7, 2020 at 12:54 | history | answered | Itamar Mushkin | CC BY-SA 4.0 |