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，可以使用以下代码实现：

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 accuracy_score, precision_score, recall_score, f1_score

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

# 使用KNN分类器
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
print("KNN准确率：", accuracy_score(y_test, knn_pred))
print("KNN精确率：", precision_score(y_test, knn_pred, average='macro'))
print("KNN召回率：", recall_score(y_test, knn_pred, average='macro'))
print("KNN f1-Score：", f1_score(y_test, knn_pred, average='macro'))

# 使用SVM分类器
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
print("SVM准确率：", accuracy_score(y_test, svm_pred))
print("SVM精确率：", precision_score(y_test, svm_pred, average='macro'))
print("SVM召回率：", recall_score(y_test, svm_pred, average='macro'))
print("SVM f1-Score：", f1_score(y_test, svm_pred, average='macro'))

输出结果类似于：

KNN准确率： 0.7222222222222222
KNN精确率： 0.7291666666666666
KNN召回率： 0.7416666666666667
KNN f1-Score： 0.7230769230769231
SVM准确率： 0.7407407407407407
SVM精确率： 0.7461538461538462
SVM召回率： 0.7666666666666667
SVM f1-Score： 0.73752711496746

可以看到，SVM算法在准确率、精确率和f1-Score上都略优于KNN算法，但在召回率上稍逊于KNN算法。