![]() ![]() These are only the calculations for one example tree, not including all potential splits. \ where \(G\) is the Gini index for each node and \(P\) is the proportion of the data each split takes up relative to the parent node.Ĭontinuing the above example, the decrease in impurity for the first split on Petal.Length is: The mean decrease in impurity (Gini) importance metric describes the improvement in the “Gini gain” splitting criterion (for classification only), which incorporates a weighted mean of the individual trees’ improvement in the splitting criterion produced by each variable The gini impurity index is defined as: Mean decrease in impurity (Gini) importance ![]() If the predictor variables in your model are highly correlated, conditional permutation importance is suggested. The authors suggest using permutation importance instead of the default in these cases. The papers and blog post demonstrate how continuous and high cardinality variables are preferred in mean decrease in impurity importance rankings, even if they are equally uninformative compared to variables with less categories. This has actually been known for over ten years ( Strobl et al, 2007 and Strobl et al, 2008), but it can be easy to assume the default importances of popular packages will fit your unique datasets. It is also known that importance metrics are biased when predictor variables are highly correlated, leading to suboptimal predictor variables being artificially preferred. ![]() Particularly, mean decrease in impurity importance metrics are biased when potential predictor variables vary in their scale of measurement or their number of categories. A recent blog post from a team at the University of San Francisco shows that default importance strategies in both R (randomForest) and Python (scikit) are unreliable in many data scenarios. ![]()
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