five modes duplication

C4.5_test.py

@@ -0,0 +1,47 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 22:28
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn import preprocessing
+from sklearn import tree
+from sklearn.metrics import classification_report
+
+# 加载CSV文件
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+# 将类别转换为数字标签
+# le = preprocessing.LabelEncoder()
+# data['label'] = le.fit_transform(data['label'])
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+# 分离特征和类别
+X = data.iloc[:, :-1]
+y = data.iloc[:, -1]
+
+# 划分数据集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+# 初始化分类器并训练模型
+clf = DecisionTreeClassifier(criterion="entropy")
+clf.fit(X_train, y_train)
+
+# 预测测试数据集
+y_pred = clf.predict(X_test)
+
+# 评估分类器的性能
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+# 可视化决策树
+tree.plot_tree(clf)
+
+#Accuracy: 0.8841506751954513
+#               precision    recall  f1-score   support
+#
+#            0       0.89      0.86      0.87      2708
+#            1       0.88      0.90      0.89      2920
+#
+#     accuracy                           0.88      5628
+#    macro avg       0.88      0.88      0.88      5628
+# weighted avg       0.88      0.88      0.88      5628