是否可以通过sklearn中的make_pipeline使用gridsearchCV执行超参数调整

from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

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

# 创建一个Pipeline
pipeline = make_pipeline(
    StandardScaler(),  # 数据标准化
    SVC()  # 支持向量机模型
)

# 定义要搜索的超参数网格
param_grid = {
    'svc__C': [0.1, 1, 10],  # SVM的正则化参数
    'svc__kernel': ['linear', 'rbf'],  # 核函数类型
    'svc__gamma': ['scale', 'auto']  # 核函数系数
}

# 创建GridSearchCV对象
grid_search = GridSearchCV(pipeline, param_grid, cv=5)

# 执行网格搜索
grid_search.fit(X, y)

# 输出最佳参数和最佳得分
print("Best parameters found: ", grid_search.best_params_)
print("Best cross-validation score: {:.2f}".format(grid_search.best_score_))