Timeline for knowing which predictors are significant in a logistic regression model
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 26, 2020 at 2:05 | vote | accept | cgo | ||
| Mar 25, 2020 at 4:25 | comment | added | Demetri Pananos | If you’ve found this helpful, please upvote and accept the abswer | |
| Mar 25, 2020 at 3:37 | comment | added | cgo | Thank you for your help. I will look into this. | |
| Mar 25, 2020 at 3:14 | comment | added | Demetri Pananos | Ah, then things are easier. My advice is to use a lasso model for classification. The penalty will take care of any separation problems, so the model will converge and you won't have to remove any variables. To identify strong predictors, I would encourage you to look at the coefficient paths, | |
| Mar 25, 2020 at 3:12 | comment | added | cgo | Thanks for your answer. The ultimate goal is to classify, and I think it is an added bonus to be able to clearly identify relationships among the predictors that influence the classification. | |
| Mar 25, 2020 at 3:05 | history | answered | Demetri Pananos | CC BY-SA 4.0 |