要求学生掌握《人工智能概论》课程中的基本知识点，了解常用的人工智能方法，能够查找资料，完成相关算法设计。基于以上知识点，使用决策树算法（ID3、C4.5、CART三选一）方法对iris数据集进行分类。

好的，基于以上要求，我可以给您提供一个简单的决策树分类算法的代码示例，用来对iris数据集进行分类。这里我选择使用ID3算法。代码如下：

import pandas as pd
import numpy as np

# 导入数据集
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pd.read_csv(url, names=names)

# 划分训练集和测试集
train = dataset.sample(frac=0.8, random_state=1)
test = dataset.drop(train.index)

# 定义节点类
class Node:
    def __init__(self, data, labels):
        self.data = data  # 当前节点的数据集
        self.labels = labels  # 当前节点的标签列表
        self.children = {}  # 当前节点的子节点
        self.split_feature = None  # 当前节点的分裂属性
        self.majority_class = None  # 当前节点所属的多数类

# 计算信息熵
def entropy(labels):
    n_labels = len(labels)
    if n_labels <= 1:
        return 0
    counts = np.bincount(labels)
    probs = counts / n_labels
    n_classes = np.count_nonzero(probs)
    if n_classes <= 1:
        return 0
    ent = 0.
    for i in probs:
        ent -= i * np.log2(i)
    return ent

# 计算信息增益
def info_gain(data, labels, split_feature):
    base_entropy = entropy(labels)
    split_entropy = 0.
    n_data = len(labels)
    for value in set(data[:, split_feature]):
        sub_data = data[data[:, split_feature] == value]
        sub_labels = labels[data[:, split_feature] == value]
        split_entropy += len(sub_labels) / n_data * entropy(sub_labels)
    return base_entropy - split_entropy

# 选择最优分裂属性
def choose_feature(node):
    data = node.data
    labels = node.labels
    n_features = data.shape[1]
    best_feature = None
    best_info_gain = -np.inf
    for i in range(n_features):
        current_info_gain = info_gain(data, labels, i)
        if current_info_gain > best_info_gain:
            best_feature = i
            best_info_gain = current_info_gain
    return best_feature

# 构建决策树
def build_tree(node):
    data = node.data
    labels = node.labels
    n_labels = len(labels)
    n_classes = len(set(labels))
    if n_labels == 0:
        node.majority_class = -1
        return
    if n_classes == 1:
        node.majority_class = labels[0]
        return
    if data.shape[1] == 0:
        node.majority_class = np.argmax(np.bincount(labels))
        return
    node.split_feature = choose_feature(node)
    for value in set(data[:, node.split_feature]):
        sub_data = data[data[:, node.split_feature] == value]
        sub_labels = labels[data[:, node.split_feature] == value]
        node.children[value] = Node(sub_data, sub_labels)
        build_tree(node.children[value])

# 预测
def predict(node, sample):
    if not node.children:
        return node.majority_class
    if sample[node.split_feature] not in node.children:
        return node.majority_class
    return predict(node.children[sample[node.split_feature]], sample)

# 计算准确率
def accuracy(tree, test):
    correct = 0
    for i in range(test.shape[0]):
        if predict(tree, test.iloc[i, :-1]) == test.iloc[i, -1]:
            correct += 1
    return correct / test.shape[0]

# 主函数
if __name__ == '__main__':
    # 划分训练集和测试集
    train = dataset.sample(frac=0.8, random_state=1)
    test = dataset.drop(train.index)
    # 构建决策树
    root = Node(train.iloc[:, :-1].values, train.iloc[:, -1].values)
    build_tree(root)
    # 测试并输出准确率
    acc = accuracy(root, test)
    print("Accuracy:", acc)

需要注意的是，这段代码使用ID3算法来构建决策树，并对iris数据集进行分类。如果您需要使用C4.5或CART算法，只需要在代码中进行相应的修改即可。另外，您还需要安装pandas和numpy这两个库。