Ансамбль деревьев

function mse_imp(trees, X, y)
    n_features = size(X, 2)
    n_samples = size(X, 1)
    importance = zeros(n_features)
    for tree in trees
        if tree isa DecisionTree.Node
            function collect_importance(node)
                if node isa DecisionTree.Node
                    if !node.left.isleaf && !node.right.isleaf
                        feat_id = node.feat_id
                        if !isnothing(feat_id) && feat_id > 0 && feat_id <= n_features
                            n_node = node.n
                            mse_current = node.score
                            mse_left = node.left.score
                            mse_right = node.right.score
                            mse_improvement = (n_node / n_samples) * 
                                             (mse_current - 
                                              (node.left.n / n_node) * mse_left - 
                                              (node.right.n / n_node) * mse_right)
                            importance[feat_id] += max(0, mse_improvement)
                        end
                    end
                    if !node.left.isleaf
                        collect_importance(node.left)
                    end
                    if !node.right.isleaf
                        collect_importance(node.right)
                    end
                end
            end
            collect_importance(tree)
        end
    end
    if sum(importance) > 0
        importance = importance / sum(importance)
    end
    
    return importance
end