Timeline for Use cross-validation to train after model selection
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Oct 17, 2018 at 12:16 | comment | added | Ruchit Dalwadi | @NeilSlater, I and DuttaA have created a chat room where we need your help. this chatroom goes by the name "Discussion by Ruchit Dalwadi". Can you join us & help ? | |
| Aug 30, 2018 at 11:53 | comment | added | user9947 | @JohnDoucette over fitting and all parameters have already been addressed by the question, I have never heard of ML models becoming worse by more data (when the best model has already been selected), the TA's answer is available in public domain videos so I doubt he is wrong...And I added neilslaters part in my answer | |
| Aug 30, 2018 at 11:25 | comment | added | John Doucette | @DuttaA Neil is correct. The reason you don't retrain on the whole set when reporting for academic writing is the same reason you wouldn't do it in most industrial settings: you have no idea whether the model has overfit, and to what degree, if you don't have a validation set for it. It is actually extremely common for simpler ML models to become worse when more data is added, and you can do this with Deep Networks too if you are not careful. Your TA is wrong, but you might be able to get away with this if you use techniques like dropout to combat overfitting, and small scale testing. | |
| Aug 30, 2018 at 4:05 | history | edited | user9947 | CC BY-SA 4.0 |
added 281 characters in body
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| Aug 29, 2018 at 21:36 | vote | accept | gcorso | ||
| Aug 29, 2018 at 18:45 | comment | added | user9947 | @NeilSlater yes ..But then we have already made the choice of going along with the model, I think when we'll put the model on real data then 100% is used, but for reporting purposes in Scientific papers it'll be 80% like u said | |
| Aug 29, 2018 at 17:18 | comment | added | Neil Slater | There is a problem with using 100% of your training set for training. That is, you have no unbiased measure of the newly-trained model's performance. This may or may not be important if all you care about is "this is probably the best model", but I think you should note that caveat in your second paragraph. | |
| Aug 29, 2018 at 16:44 | history | answered | user9947 | CC BY-SA 4.0 |