Python代码实现ID3算法和改进的C4.5算法

ID3（Iterative Dichotomiser 3）算法是一种基于信息熵的决策树生成算法，用于分类任务。它的核心思想是每次通过计算特征的信息增益或信息增益比来选择最优划分属性。以下是Python简单版本的ID3算法伪代码示例：

def id3(data, features, target):
    if data.is_empty() or len(set(target)) == 1:
        return leaf_node(target)

    best_gain = 0
    best_feature = None

    for feature in features:
        gain = information_gain(data, feature, target)
        if gain > best_gain:
            best_gain = gain
            best_feature = feature

    if best_gain == 0:
        return leaf_node(most_common_value(target))

    tree = {best_feature: {}}
    feature_values = data.distinct(best_feature)

    for value in feature_values:
        subtree_data = data.where(best_feature, value)
        tree[best_feature][value] = id3(subtree_data, remove(feature, features), target)

    return tree

# ...其他辅助函数...

C4.5算法是对ID3的改进，主要是添加了剪枝机制，防止过拟合。它引入了基于最小描述长度的原则的“后向归纳”剪枝策略，同时支持连续值特征。要实现在Python中C4.5，你需要一个包含节点分裂、剪枝等复杂逻辑的完整实现，例如`sklearn`库中的`DecisionTreeClassifier`就是C4.5的一个实现。

from sklearn.tree import DecisionTreeClassifier

# 创建C4.5树模型
c45_tree = DecisionTreeClassifier(criterion='gini', max_depth=None)

# 训练模型
c45_tree.fit(X_train, y_train)

# 使用模型预测
y_pred = c45_tree.predict(X_test)