如何计算K折交叉验证中所有折叠的分类报告中的平均值？

from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report

# 假设有一个名为X的特征矩阵和一个名为y的目标向量

# 创建分类器（例如，支持向量机）
classifier = ...

# 进行K折交叉验证，并计算分类报告
scores = cross_val_score(classifier, X, y, cv=K, scoring='accuracy')
reports = []
for train_index, test_index in K折交叉验证的索引:
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    classifier.fit(X_train, y_train)
    y_pred = classifier.predict(X_test)
    report = classification_report(y_test, y_pred, output_dict=True)
    reports.append(report)

# 计算分类报告的平均值
average_report = {}
for label in reports[0].keys():
    average_report[label] = {}
    for metric in reports[0][label].keys():
        values = [report[label][metric] for report in reports]
        average_report[label][metric] = sum(values) / len(values)

# 打印平均分类报告
for label, metrics in average_report.items():
    print(label)
    for metric, value in metrics.items():
        print(metric, value)
    print()