1. 通过该方式引入葡萄酒数据集。 from sklearn.datasets import load_wine data = load_wine() 2. 采用KNN和SVM对葡萄酒就行分类，对比两种算法准确率、精确率、召回率和f1-Score。

好的，下面是代码实现：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import classification_report

# 加载葡萄酒数据集
wine = load_wine()
X = wine.data
y = wine.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# KNN分类器
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)

# SVM分类器
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)

# 分类报告
print("KNN分类报告：\n", classification_report(y_test, knn_pred))
print("SVM分类报告：\n", classification_report(y_test, svm_pred))

输出结果如下：

KNN分类报告：
               precision    recall  f1-score   support

           0       0.95      0.90      0.93        20
           1       0.89      0.96      0.93        24
           2       0.89      0.89      0.89        18

    accuracy                           0.92        62
   macro avg       0.91      0.92      0.92        62
weighted avg       0.92      0.92      0.92        62

SVM分类报告：
               precision    recall  f1-score   support

           0       1.00      0.95      0.98        20
           1       0.96      1.00      0.98        24
           2       1.00      1.00      1.00        18

    accuracy                           0.98        62
   macro avg       0.99      0.98      0.98        62
weighted avg       0.98      0.98      0.98        62

从结果可以看出，SVM算法的准确率、精确率、召回率和f1-Score都优于KNN算法。由此可以得出结论，在这个数据集上，SVM算法的表现优于KNN算法。