Dear SciKitters,
how does a sklearn RF actually compute the feature importances? Can I
assume that the feature contribution (the final leaf of a single model) is
averaged over all trained single models and then outputted?
How does it compare to the "R way"?
Here, I see that the "MeanDecreaseGini" is plotted to dig out the relevant
features. Can this be done by sklearn as well, or are the methods
comparable?
Cheers & Thanks,
Paul
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