features_importances_
always output the importance of the features. If the value is bigger, more important is the feature, don't take in consideration gini or entropy criterion, it doesn't matter. Criterion is used to build the model. Feature importance is applied after the model is trained, you only "analyze" and observe which values have been more relevant in your trained model.
Moreover, you will see that all features_importances_
sums to 1, so the importance is seen as a percentage too.
Since RandomForest is formed by several trees, feature importances are averaged over all the trees.
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