Timeline for Feature selection on large file with missing categorical and numerical data in R
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Oct 13, 2015 at 21:41 | comment | added | Chamberlain Mbah | If you are in for prediction then you can use the lasso both for feature selection and parameter estimation. In that case your biased estimates will then have a small out-sample variance. Please note feature selection is a goal in itself. | |
| Oct 13, 2015 at 21:39 | comment | added | Chamberlain Mbah | Feature selection with lasso is perfect. The parameter estimates are biased all in one direction. The selected set of predictors are the best. This subset is ordered that means if it is too small, reducing shrinkage will add more variables. The existing variables will remain in the set. You can then go ahead to do unbiased estimation with modeling without penalization on the subset if you are in for hypothesis testing. The lasso is perfect for feature selection. But if you need unbiased estimates, you already have the subset to work from. | |
| Oct 13, 2015 at 21:19 | history | edited | Chamberlain Mbah | CC BY-SA 3.0 |
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| Oct 13, 2015 at 18:54 | comment | added | Sergey Bushmanov | @ChamberlainFoncha Think twice before using Lasso for feature selection, it wasn't made for this. Lasso uses regularization to induce some bias to ensure better out-of-sample generalization of the Lasso model, not other models. If you carry over features selected by Lasso to other models, there is absolutely no guarantee this set of features will perform well. | |
| Oct 13, 2015 at 9:00 | comment | added | Chamberlain Mbah | Feature selection: the "LASSO" will be very suitable for this job. I am a big fan of Hastie and Tisbshirani. Use the $glmnet$ package. It will require patience. | |
| Oct 13, 2015 at 8:45 | comment | added | user90772 | Thank you Chamberlain, I will try this. I was wondering if you tried some feature selection on this dataset. | |
| Oct 13, 2015 at 7:37 | history | answered | Chamberlain Mbah | CC BY-SA 3.0 |