StratifiedKFold的混淆矩阵和分类报告

from sklearn.datasets import load_iris
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression

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

# 初始化模型
model = LogisticRegression(max_iter=200)

# 初始化 StratifiedKFold
skf = StratifiedKFold(n_splits=5)

# 存储所有折的预测结果
all_preds = []
all_targets = []

# 进行交叉验证
for train_index, test_index in skf.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # 训练模型
    model.fit(X_train, y_train)
    
    # 预测
    preds = model.predict(X_test)
    
    # 存储预测结果和真实标签
    all_preds.extend(preds)
    all_targets.extend(y_test)

# 生成混淆矩阵
conf_matrix = confusion_matrix(all_targets, all_preds)
print("Confusion Matrix:")
print(conf_matrix)

# 生成分类报告
class_report = classification_report(all_targets, all_preds)
print("Classification Report:")
print(class_report)