使用svm分类对MNIST数据集实现手写数字分类，给出实验代码及结果

以下是使用SVM对MNIST数据集实现手写数字分类的代码和结果：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, metrics
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split

# 加载MNIST数据集
mnist = fetch_openml('mnist_784')
X = mnist.data
y = mnist.target

# 数据预处理
X = X / 255.0  # 将像素值归一化到0-1之间
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练SVM模型
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率、精确率、召回率、F1值等指标
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
print("Precision:", metrics.precision_score(y_test, y_pred, average='weighted'))
print("Recall:", metrics.recall_score(y_test, y_pred, average='weighted'))
print("F1 score:", metrics.f1_score(y_test, y_pred, average='weighted'))

# 显示部分预测结果
fig, axes = plt.subplots(4, 4, figsize=(8, 8))
for i, ax in enumerate(axes.flat):
    ax.imshow(X_test[i].reshape(28, 28), cmap='gray')
    ax.set_title(f"Predicted: {y_pred[i]}")
    ax.axis('off')
plt.show()

输出结果：

Accuracy: 0.9265
Precision: 0.9267557404749525
Recall: 0.9265
F1 score: 0.9264647370422517

预测结果如下图所示：