Timeline for Imputing missing values on a testing set
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| S Sep 14 at 20:47 | history | suggested | cottontail | CC BY-SA 4.0 |
link to archive (original link is dead)
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| Sep 14 at 18:15 | review | Suggested edits | |||
| S Sep 14 at 20:47 | |||||
| Sep 5, 2017 at 15:20 | comment | added | Dimgold | All your training data shoud be available during the test phase (and in fact is implicitly used in your model). Just note that in case of k-fold CV the train and test might switch roles numerous times | |
| Sep 5, 2017 at 7:38 | comment | added | pd441 | Many thanks for your response. However, wouldn't the use of the training mean to impute for both/either or missing values and and outliers on the testing set be a kind of data leakage to the test set? Then the model would of course perform better than it should because there is data in the testing set that is based upon training data (the same data used to create said model)? | |
| Sep 5, 2017 at 7:25 | vote | accept | pd441 | ||
| Sep 5, 2017 at 7:26 | |||||
| Sep 5, 2017 at 7:25 | vote | accept | pd441 | ||
| Sep 5, 2017 at 7:25 | |||||
| Sep 4, 2017 at 15:41 | history | answered | Dimgold | CC BY-SA 3.0 |