如何评估分类器的精度，并在GridSearchCV中留下一条roc曲线？

from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# 准备数据集
X_train, X_test, y_train, y_test = ...

# 创建分类器模型
classifier = SVC()

# 创建参数网格
param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf']
}

# 创建GridSearchCV对象
grid_search = GridSearchCV(classifier, param_grid, scoring='accuracy')

# 训练模型
grid_search.fit(X_train, y_train)

# 获取最佳模型
best_model = grid_search.best_estimator_

# 预测测试集数据
y_pred = best_model.predict(X_test)

# 计算ROC曲线的真正例率和假正例率
fpr, tpr, thresholds = roc_curve(y_test, y_pred)

# 计算AUC值
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()