Timeline for Analysis of Feature Importances when features are dependent on one another
Current License: CC BY-SA 3.0
4 events
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Nov 30, 2014 at 18:39 | comment | added | katya | The suggested rfe reference is just a way to explore how much it matters in this particular case, there are other ways. In my experience, RF is very robust to this problem (so I certainly don't recommend supplanting it), but as the first reference shows it is not always immune. And another consideration is why correlated predictors were included: whether they are necessary and meaningful in the context of your model or whether condensing or more carefully chosing them would be more justified. | |
| Nov 30, 2014 at 18:14 | comment | added | user46925 | From my experience, random forests seems to give me more realistic feature importances. - But in theory should RFE do better? Would you always recommend RFE over RF? | |
| Nov 30, 2014 at 17:43 | history | answered | katya | CC BY-SA 3.0 |