# Interpretation of feature selection based on the model

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.

With a depth of 3.

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.

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