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Since decision tree don't use all the input features and select them in the process, is it useful to do feature selection before?

As I see it, choosing features will decrease computing time (and decrease overfitting risk on small dataset?), but as multiple weak features can perform better than strong ones, I may also have a worse prediction.

EDIT : Bonus question : Is there a way to select features before a decision tree, or should I let it do the work ?

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  • $\begingroup$ The question is how can you select them before the decision tree? $\endgroup$ – Metariat Sep 19 '16 at 7:36
  • $\begingroup$ I was thinking using a feature selection technique, maybe LASSO or something else $\endgroup$ – CoMartel Sep 19 '16 at 7:46
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    $\begingroup$ Variables that are important in LASSO don't necessarily have the same relationship with the outcome as in decision tree. You can see a related question here: stats.stackexchange.com/questions/164048/… $\endgroup$ – Metariat Sep 19 '16 at 7:53
  • $\begingroup$ Ok, I see your point. The remaining question is : is there a way to select features before a decision tree, or should I let it do the work ? $\endgroup$ – CoMartel Sep 19 '16 at 8:11
  • $\begingroup$ In my personal experienc, I don't see any way to select features before building the tree. $\endgroup$ – Metariat Sep 19 '16 at 8:12
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Decision Trees are pretty good at finding the most important features, they consider all features and create a split on the one that is separating class labels the best (in terms of entropy).

If you use Random Forests it's even better, because some implementations (like scikit-learn's) are capable of sampling the features and use only a subset of it. Also in general Random Forests are more robust than decision trees.

If you want, you can compute Information Gain before using a Decision Tree to see how much information a particular feature contains regarding the Label:

https://en.wikipedia.org/wiki/Information_gain_in_decision_trees

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