scikit learn - How are feature_importances in RandomForestClassifier determined?

Question

I have a classification task with a time-series as the data input, where each attribute (n=23) represents a specific point in time. Besides the absolute classification result I would like to find out, which attributes/dates contribute to the result to what extent. Therefore I am just using the feature_importances_, which works well for me.

However, I would like to know how they are getting calculated and which measure/algorithm is used. Unfortunately I could not find any documentation on this topic.

Answer 1

There are indeed several ways to get feature "importances". As often, there is no strict consensus about what this word means.

In scikit-learn, we implement the importance as described in [1] (often cited, but unfortunately rarely read...). It is sometimes called "gini importance" or "mean decrease impurity" and is defined as the total decrease in node impurity (weighted by the probability of reaching that node (which is approximated by the proportion of samples reaching that node)) averaged over all trees of the ensemble.

In the literature or in some other packages, you can also find feature importances implemented as the "mean decrease accuracy". Basically, the idea is to measure the decrease in accuracy on OOB data when you randomly permute the values for that feature. If the decrease is low, then the feature is not important, and vice-versa.

(Note that both algorithms are available in the randomForest R package.)

[1]: Breiman, Friedman, "Classification and regression trees", 1984.