Timeline for Leave one out Cross validation using sklearn (Multiple CSV)
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Dec 18, 2020 at 17:21 | comment | added | Sapiens | @PhillipCopley I see, you can indeed have several observations for the subject left out. | |
| Nov 19, 2020 at 18:19 | comment | added | Phillip Copley | @Sapiens You get a score each test, and you can then average the scores. | |
| Nov 13, 2020 at 22:15 | comment | added | Sapiens | Why does one get several scores? Is the algorithm calculating the performance score on the test set? and not on the left out validation observations? Otherwise, it should produce only one score, no? (I may create a separate question for this, later). | |
| Jun 13, 2020 at 6:28 | comment | added | Talha Anwar |
scores = cross_val_score(classifier , X = input data , y = target values , cv = Kfold(X.shape[0]))
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| Jun 13, 2020 at 6:20 | comment | added | Talha Anwar |
it give me an error ValueError: n_splits=56 cannot be greater than the number of members in each class. whereas 56 is X.shape[0]
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| May 25, 2019 at 5:09 | vote | accept | Bloodstone Programmer | ||
| Jun 30, 2018 at 12:05 | review | Late answers | |||
| Jun 30, 2018 at 14:34 | |||||
| Jun 30, 2018 at 11:48 | history | answered | Faraz Gerrard Jamal | CC BY-SA 4.0 |