使用scikit learn的SGDClassifier实现支持向量机:如何调整正则化参数？

from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 定义参数网格
param_grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]}

# 创建SGDClassifier对象
clf = SGDClassifier(loss='hinge', penalty='l2', random_state=42)

# 使用GridSearchCV进行交叉验证
grid_search = GridSearchCV(clf, param_grid, cv=5)
grid_search.fit(X, y)

# 输出最佳参数和对应的得分
print("Best alpha:", grid_search.best_params_['alpha'])
print("Best score:", grid_search.best_score_)