每日总结49

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split, cross_val_score

from sklearn.neural_network import MLPClassifier

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

 

# 加载iris数据集

iris = load_iris()

X, y = iris.data, iris.target

 

# 使用留出法留出1/3的样本作为测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=42)

 

# 初始化多层感知机分类器（BP神经网络）

mlp = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', solver='adam', random_state=42)

 

# 训练模型

mlp.fit(X_train, y_train)

 

# 使用五折交叉验证评估模型性能

scores = cross_val_score(mlp, X_train, y_train, cv=5, scoring='accuracy')

 

# 预测测试集

y_pred = mlp.predict(X_test)

 

# 计算准确度、精度、召回率和F1值

accuracy = accuracy_score(y_test, y_pred)

precision = precision_score(y_test, y_pred, average='macro')

recall = recall_score(y_test, y_pred, average='macro')

f1 = f1_score(y_test, y_pred, average='macro')

 

# 打印结果

print(f"五折交叉验证准确度: {scores.mean():.2f} +/- {scores.std():.2f}")

print(f"测试集准确度: {accuracy:.2f}")

print(f"测试集精度: {precision:.2f}")

print(f"测试集召回率: {recall:.2f}")

print(f"测试集F1值: {f1:.2f}")