在MLPClassification Python中实现K折交叉验证

from sklearn.model_selection import KFold
from sklearn.neural_network import MLPClassifier

# 假设X为特征数据，y为标签数据
X = ...
y = ...

# 定义MLPClassifier模型
model = MLPClassifier()

# 定义K折交叉验证
kfold = KFold(n_splits=K, shuffle=True)

# 进行K折交叉验证
for train_index, test_index in kfold.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # 在训练集上训练模型
    model.fit(X_train, y_train)
    
    # 在验证集上进行预测
    y_pred = model.predict(X_test)
    
    # 计算评估指标
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    
    # 打印评估结果
    print("Accuracy: ", accuracy)
    print("Precision: ", precision)
    print("Recall: ", recall)