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I have several random forest models that work well. Now, I would like to do feature selection based on this models. How do I find out which features were used frequently by a model e.g. RandomForestClassifier in scikit-learn?

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Feature importance it is a property of the random forrest classifier.

See an example here

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Just to add to the feature Importance of the RF's , don't forget to use the hierarchical clustering from scipy and plot the dendograms..

It will help you find the similar columns..

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  • $\begingroup$ You mean in order to not use strongly correlated columns? $\endgroup$
    – Soerendip
    Mar 1, 2018 at 12:59
  • $\begingroup$ Plotting the dendogram will reveal the similar columns so you can take a sneak peak at them and them do experiments like dropping one of them let say and then re run the model and recur $\endgroup$
    – Aditya
    Mar 1, 2018 at 13:11
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Try looking at the DALEX package for R. It is designed to specifically investigate the importance of input variables to perhaps otherwise opaque ML algos.

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