I’d say the probably best summary (and discussion) can be found 
"Understanding variable importances in forests of randomized trees” by Gilles 
Louppe, Louis Wehenkel, Antonio Sutera and Pierre Geurts (with references to 
Breimans original proposed ideas)
http://papers.nips.cc/paper/4928-understanding-variable-importances-in-forests-of-randomized-trees.pdf
 

Best,
Sebastian


> On May 16, 2016, at 5:49 PM, Gavin Gray <s0805...@sms.ed.ac.uk> wrote:
> 
> In the Scikit-Learn documentation the feature importances are described as 
> coming from the relative depths features are used as decision nodes, averaged 
> across trees in the forest. Does anyone know which paper discusses this 
> method? Breiman's original paper seems to just talk about randomly permuting 
> the values of each variable and observing the change in the objective 
> function.
> 
> Thanks,
> -Gavin
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