Timeline for Pre-processing - Removing outliers
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 25, 2020 at 18:52 | comment | added | fuwiak | No, you have to do it manually. No general rule, you should try a lot of options and find the best. | |
| Apr 25, 2020 at 18:50 | comment | added | Afia R. S. | If I drop them from my training data, then will similar points in the test files be correctly labeled? Is there a general best practice, when one has such data in both test and train files? | |
| Apr 25, 2020 at 18:34 | comment | added | fuwiak | Rusoiba, outliers sometimes they are the most important data of all, how do you justify removing most valuable data from the dataset? | |
| Apr 25, 2020 at 18:26 | comment | added | Rusoiba | I do not agree. It could be ok depending on the case. If you are interested in predicting a limited range in the case of a regression, you will achieve best results by dropping outliers (observations out of the range). | |
| Apr 25, 2020 at 18:26 | comment | added | fuwiak | About "other models"- it depends on your data and assumptions, I can't answer without more details. "data transformation"- try for example log transformation, sample have you in this link: chrisalbon.com/machine_learning/preprocessing_structured_data/… | |
| Apr 25, 2020 at 18:15 | comment | added | Afia R. S. | Thanks, could you elaborate on "other models" or "data transformation"? May be you could provide a link to some tutorials? I am not familiar with any of them | |
| Apr 25, 2020 at 17:52 | history | answered | fuwiak | CC BY-SA 4.0 |