用ID3算法生成的决策树，进行后剪枝，后剪枝的代码怎么写

以下是一个简单的后剪枝代码示例： ```python def prune(tree, X_val, y_val): """ Prune the decision tree using the validation set X_val, y_val """ if tree.is_leaf: return # Recursively prune the left and right subtrees prune(tree.left, X_val, y_val) prune(tree.right, X_val, y_val) # Check if pruning the subtree resulted in better accuracy if not tree.left.is_leaf or not tree.right.is_leaf: pred = tree.predict(X_val) acc_before = accuracy_score(y_val, pred) # Prune the subtree by converting it to a leaf node with majority class label tree.left = None tree.right = None tree.is_leaf = True tree.pred = np.argmax(np.bincount(y_val)) # Check accuracy again after pruning pred = tree.predict(X_val) acc_after = accuracy_score(y_val, pred) # If accuracy decreased after pruning, revert the subtree back to unpruned if acc_after < acc_before: tree.is_leaf = False left_indices = X_val[:, tree.feature] < tree.threshold right_indices = X_val[:, tree.feature] >= tree.threshold tree.left = DecisionTree(X_val[left_indices, :], y_val[left_indices]) tree.right = DecisionTree(X_val[right_indices, :], y_val[right_indices]) tree.left.split() tree.right.split() # Example usage tree = DecisionTree(X_train, y_train) tree.split() prune(tree, X_val, y_val) ```