Timeline for Feature Selection - Overfit?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 26, 2020 at 23:25 | history | edited | DanielTheRocketMan | CC BY-SA 4.0 |
added 573 characters in body
|
| Feb 26, 2020 at 23:23 | comment | added | DanielTheRocketMan | It is ok. An option to do that ... See the edition. Just a minute. | |
| Feb 26, 2020 at 23:20 | comment | added | Luis Pinto | For every 70% of the train set, I pass it through a classifier and take the features passing a feature threshold (I use weights or feature importance) and create a counter out of these lists. I use "SelectFromModel" function from sklearn.feature_selection package. | |
| Feb 26, 2020 at 23:15 | comment | added | DanielTheRocketMan | I cannot see any problem in your choice. It is not just very clear how you choose your best features. | |
| Feb 26, 2020 at 23:14 | history | edited | DanielTheRocketMan | CC BY-SA 4.0 |
deleted 29 characters in body
|
| Feb 26, 2020 at 23:10 | comment | added | Luis Pinto | In step 3, I am splitting the train test into 70-30 X times. That means that at the end I do use all the train set to find the subset of features. In step 4, I do k-fold cv on the 80% train (i.e. all data points from step 3) | |
| Feb 26, 2020 at 22:39 | history | answered | DanielTheRocketMan | CC BY-SA 4.0 |