使用knn对sklearn wine数据集 进行分类

好的，我们可以按照以下步骤使用KNN算法对sklearn中的wine数据集进行分类：

1. 导入所需的库和数据集：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

wine = load_wine()
X = wine.data
y = wine.target

2. 将数据集划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. 创建KNN模型并进行训练：

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

4. 使用训练好的模型进行预测并计算准确率：

y_pred = knn.predict(X_test)
accuracy = knn.score(X_test, y_test)

print("预测结果：", y_pred)
print("准确率：", accuracy)

完整代码如下：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

wine = load_wine()
X = wine.data
y = wine.target

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

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

y_pred = knn.predict(X_test)
accuracy = knn.score(X_test, y_test)

print("预测结果：", y_pred)
print("准确率：", accuracy)