使用knn对sklearn wine数据集 进行分类。

好的，我来给你介绍一下如何使用knn对sklearn wine数据集进行分类。

首先，我们需要导入需要的库和数据集。代码如下：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

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

然后，我们需要将数据集分成训练集和测试集。这可以通过使用train_test_split()函数来完成。代码如下：

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

接下来，我们需要对数据集进行标准化处理，这可以使用StandardScaler()函数来进行。代码如下：

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

然后，我们可以使用KNeighborsClassifier()函数来创建一个knn分类器，并且使用fit()函数将训练集的数据和标签传递给分类器。代码如下：

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

最后，我们可以使用predict()函数来预测测试集的标签，并且使用accuracy_score()函数来计算模型的准确率。代码如下：

y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

这样，我们就可以使用knn对sklearn wine数据集进行分类了。完整代码如下：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

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)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

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

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

print("Accuracy:", accuracy)