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The description of feature selection based on a random forest uses trees without pruning. Do I need to use tree pruning? The thing is, if I don't cut the trees, the forest will retrain.

Below in the picture is the importance of features based on 500 trees without pruning. enter image description here

With a depth of 3. enter image description here

I always use the last four signs 27, 28, 29, 30. And I try to add to them signs from 0 to 26 by means of cycles, going through possible combinations. Empirically, I assume that the trait number 0, 26 is significant. But, on both pictures it is not visible. Although the quality of classification with the addition of 0, 26 has improved.

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random forest's feature importances are not reliable and you should probably avoid them. Instead you can use permutation_importance: https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py

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    $\begingroup$ "random forest's feature importances are not reliable and you should probably avoid them" is a very strong statement which does not hold. While random forest variable importance estimates are known to be biased under certain conditions (e.g. gini impurity-based estimates on a mix of continuous and categorical variables), they can provide relatively good estimates in other situations. $\endgroup$ – Sammy Jan 16 at 20:31
  • $\begingroup$ @adrin thanks. I want to get the features values in "permutation_importance". As in "importances = clf.feature_importances_", to build a graph . If I request"result.importances", I get a lot of items on each index. It turns out to get only indexes. Which function should I turn to? $\endgroup$ – user287629 Jan 16 at 22:18

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