c4.5对鸢尾花数据集分类并可视化

以下是使用C4.5算法对鸢尾花数据集进行分类并可视化的步骤：

1.导入所需库和数据集

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import graphviz

iris = load_iris()
X = iris.data
y = iris.target

2.将数据集分为训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

3.使用C4.5算法构建决策树模型

clf = DecisionTreeClassifier(criterion='entropy')
clf.fit(X_train, y_train)

4.可视化决策树模型

dot_data = export_graphviz(clf, out_file=None, 
                           feature_names=iris.feature_names,  
                           class_names=iris.target_names,  
                           filled=True, rounded=True,  
                           special_characters=True)  
graph = graphviz.Source(dot_data)  
graph

5.预测测试集并计算准确率

y_pred = clf.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)