The blog post from Matthew Drury sums it up well. The feature importance is indeed the Gini impurity.
On Tue, May 9, 2017 at 8:34 AM, Olga Lyashevska <o.lyashevsk...@gmail.com> wrote: > 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/fc2f24927f > c37d7e42917369f17de045b14c59b5/sklearn/tree/_tree.pyx#L1056 > > I have also seen a post by Matthew Drury on stack exchange: > https://stats.stackexchange.com/questions/162162/relative-va > riable-importance-for-boosting > > Many thanks, > Olga > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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