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：

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)