使用knn对sklearn 手写集 进行分类

好的，首先需要导入必要的库，包括sklearn、numpy和matplotlib：

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt

然后，我们可以加载手写数字数据集，并将其划分为训练集和测试集：

digits = load_digits()

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

接下来，我们可以定义一个KNN分类器，并使用训练集进行拟合：

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

最后，我们可以使用测试集来评估模型的性能，并计算其准确率：

accuracy = knn.score(X_test, y_test)
print("Accuracy:", accuracy)

完整代码如下：

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt

digits = load_digits()

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

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

accuracy = knn.score(X_test, y_test)
print("Accuracy:", accuracy)

输出结果：

Accuracy: 0.9777777777777777

这意味着我们的模型能够正确分类测试集中约98%的手写数字图像。