# 导入库
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+
+# 定义数据集
+data = {"Age": ["<=30", "<=30", "31-40", ">40", ">40", ">40", "31-40", "<=30", "<=30", ">40", "<=30",
+ "31-40", "31-40", ">40"],
+ "Income": ["high", "high", "high", "medium", "low", "low", "low", "medium", "low", "medium",
+ "medium", "medium", "high", "medium"],
+ "Student": ["no", "no", "no", "no", "yes", "yes", "yes", "no", "yes", "yes", "yes", "no", "yes",
+ "no"],
+ "Credit_rating": ["mid", "high", "mid", "mid", "mid", "high", "high", "mid", "mid", "mid", "high",
+ "high", "mid", "high"],
+ "Buy_computer": ["no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes", "yes", "yes",
+ "yes", "no"]
+ }
+
+# 将数据集转换为DataFrame格式
+df = pd.DataFrame(data)
+
+# 将字符特征转换成数值特征
+df["Age"] = df["Age"].map({"<=30": 0, "31-40": 1, ">40": 2})
+df["Income"] = df["Income"].map({"low": 0, "medium": 1, "high": 2})
+df["Student"] = df["Student"].map({"no": 0, "yes": 1})
+df["Credit_rating"] = df["Credit_rating"].map({"low": 0, "mid": 1, "high": 2})
+df["Buy_computer"] = df["Buy_computer"].map({"no": 0, "yes": 1})
+
+# 将数据集分割成训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop(columns=["Buy_computer"]), df["Buy_computer"], test_size=0.5, random_state=33)
+
+# 初始化模型
+gnb = GaussianNB()
+
+# 训练模型
+gnb.fit(X_train, y_train)
+
+# 预测测试集结果
+y_pred = gnb.predict(X_test)
+
+# 计算准确率
+score = accuracy_score(y_test, y_pred)
+
+# 输出预测结果、实际结果和模型得分
+print("Predict\tActual")
+for i in range(len(y_test)):
+ print("{}\t{}".format(y_pred[i], y_test.iloc[i]))
+print("Model score:", score)
+
+