Timeline for Algorithm to select predictors in logistic regression?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Jan 5, 2017 at 21:32 | answer | added | dwhdai | timeline score: 2 | |
| Jan 5, 2017 at 20:13 | history | edited | Andre Silva | CC BY-SA 3.0 |
added 2 characters in body; edited tags; edited title
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| Feb 18, 2016 at 0:15 | comment | added | Sycorax♦ | If you're only getting an intercept back, that's telling you that none of the variables have a strong linear relationship with the outcome. Perhaps some interactions or basis expansions could work but basically your data is not strongly informative enough about the features that you have to justify anything more than an intercept model. The lasso is a principled approach to this problem, while what you're suggesting has serious flaws. Construct a train/test partition and compare true to predicted outcome values to see what I mean. | |
| Feb 17, 2016 at 23:33 | comment | added | Adam | I tried using the example of glmnet found here: web.stanford.edu/~hastie/glmnet/glmnet_alpha.html but, after following the steps suggested, the model selected was the intercept-only model. Just by this rudimentary approach I'm using I have found much better models so I'm not sure if LASSO would work for me here. | |
| Feb 17, 2016 at 23:19 | comment | added | Sycorax♦ | You could do worse than to use LASSO. | |
| Feb 17, 2016 at 23:18 | answer | added | Frank Harrell | timeline score: 4 | |
| Feb 17, 2016 at 23:01 | history | asked | Adam | CC BY-SA 3.0 |