如何通过GridSearchCV打印最佳参数进行k折交叉验证

from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold

# 假设X为特征数据，y为目标变量
X, y = ...

# 假设使用支持向量机(SVM)作为模型
model = SVC()
# 定义参数网格，以C和gamma为例
param_grid = {'C': [0.1, 1, 10], 'gamma': [0.001, 0.01, 0.1]}

# 创建GridSearchCV对象，传入模型、参数网格和评分指标等参数
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', cv=KFold(n_splits=k))

# 执行GridSearchCV，传入特征数据和目标变量
grid_search.fit(X, y)

# 打印最佳参数和得分
print("Best parameters: ", grid_search.best_params_)
print("Best score: ", grid_search.best_score_)