[scikit-learn] impurity criterion in gradient boosted regression trees

[Olga Lyashevska]

Hi all,

I am trying to understand differences in feature importance plots 
obtained with R package gbm and sklearn. Having compared both 
implementation side by side it seems that the models are fairly similar, 
however feature importance plots are rather distinct.

R uses empirical improvement in squared error as it is described in 
Friedmans's "Greedy Function Approximation" paper (eq. 44, 45).

sklearn (as far as I could see it in the code) uses the weighted 
reduction in node purity. How exactly is this calculated? Is it a gini 
index? Is there a reference?

I found this, but I find this hard to follow:
https://github.com/scikit-learn/scikit-learn/blob/fc2f24927fc37d7e42917369f17de045b14c59b5/sklearn/tree/_tree.pyx#L1056

I have also seen a post by Matthew Drury on stack exchange: 
https://stats.stackexchange.com/questions/162162/relative-variable-importance-for-boosting

Many thanks,
Olga