用决策树 对一个有10个标签2个类别 叫data.csv的数据集 用python实现信息增益 显示完整代码

以下是用Python实现信息增益的完整代码：

import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# 读取数据集
data = pd.read_csv('data.csv')

# 将标签列作为分类目标
X = data.drop('label', axis=1)
y = data['label']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 建立决策树模型
clf = DecisionTreeClassifier(criterion='entropy')
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = np.mean(y_pred == y_test)
print('准确率: {:.2f}%'.format(accuracy * 100))

# 输出决策树
from sklearn.tree import export_graphviz
import graphviz

dot_data = export_graphviz(
    clf, 
    out_file=None,
    feature_names=X.columns,
    class_names=['0', '1'],
    filled=True,
    rounded=True,
    special_characters=True
)

graph = graphviz.Source(dot_data)
graph.render("decision_tree")

graph

在这个代码中，`pandas`库被用于读取和处理数据集。`sklearn.tree`库提供了`DecisionTreeClassifier`类，用于建立决策树模型。`train_test_split`函数用于划分训练集和测试集。`numpy`库被用于计算准确率。`export_graphviz`函数用于输出决策树的DOT格式代码，`graphviz`库用于显示决策树。