c45决策树算法代码

当然可以！以下是一个使用C4.5决策树算法的简单代码示例：

import math

def entropy(data):
    # 计算数据集的熵值
    label_counts = {}
    for row in data:
        label = row[-1]
        if label not in label_counts:
            label_counts[label] = 0
        label_counts[label] += 1

    entropy = 0.0
    for count in label_counts.values():
        probability = count / len(data)
        entropy -= probability * math.log2(probability)
    return entropy

def split_data(data, attribute, value):
    # 根据给定的属性和属性值划分数据集
    subset = []
    for row in data:
        if row[attribute] == value:
            reduced_row = row[:attribute]
            reduced_row.extend(row[attribute+1:])
            subset.append(reduced_row)
    return subset

def choose_best_attribute(data):
    # 选择最佳划分属性
    num_attributes = len(data[0]) - 1
    base_entropy = entropy(data)
    best_info_gain = 0.0
    best_attribute = -1

    for i in range(num_attributes):
        attribute_values = [row[i] for row in data]
        unique_values = set(attribute_values)
        new_entropy = 0.0
        for value in unique_values:
            subset = split_data(data, i, value)
            probability = len(subset) / len(data)
            new_entropy += probability * entropy(subset)
        info_gain = base_entropy - new_entropy
        if info_gain > best_info_gain:
            best_info_gain = info_gain
            best_attribute = i

    return best_attribute

def majority_count(labels):
    # 计算标签列表中出现次数最多的标签
    label_counts = {}
    for label in labels:
        if label not in label_counts:
            label_counts[label] = 0
        label_counts[label] += 1
    sorted_labels = sorted(label_counts.items(), key=lambda x: x[1], reverse=True)
    return sorted_labels[0][0]

def create_decision_tree(data, attributes):
    # 创建决策树
    labels = [row[-1] for row in data]

    if labels.count(labels[0]) == len(labels):
        return labels[0]

    if len(data[0]) == 1:
        return majority_count(labels)

    best_attribute = choose_best_attribute(data)
    best_attribute_label = attributes[best_attribute]
    decision_tree = {best_attribute_label: {}}
    del(attributes[best_attribute])
    attribute_values = [row[best_attribute] for row in data]
    unique_values = set(attribute_values)

    for value in unique_values:
        subset = split_data(data, best_attribute, value)
        decision_tree[best_attribute_label][value] = create_decision_tree(subset, attributes[:])

    return decision_tree

# 示例用法
data = [['青年', '否', '否', '一般', '否'],
        ['青年', '否', '否', '好', '否'],
        ['青年', '是', '否', '好', '是'],
        ['青年', '是', '是', '一般', '是'],
        ['青年', '否', '否', '一般', '否'],
        ['中年', '否', '否', '一般', '否'],
        ['中年', '否', '否', '好', '否'],
        ['中年', '是', '是', '好', '是'],
        ['中年', '否', '是', '非常好', '是'],
        ['中年', '否', '是', '非常好', '是'],
        ['老年', '否', '是', '非常好', '是'],
        ['老年', '否', '是', '好', '是'],
        ['老年', '是', '否', '好', '是'],
        ['老年', '是', '否', '非常好', '是'],
        ['老年', '否', '否', '一般', '否']]
attributes = ['年龄', '有工作', '有自己的房子', '信贷情况']

decision_tree = create_decision_tree(data, attributes)
print(decision_tree)

希望这个简单的示例能帮助你理解C4.5决策树算法的实现！