Python编写一个代码使用 MLP 神经网络、随机森林等分类器，实现对上述训练数据集中实体预测识别

在Python中，我们可以使用scikit-learn库来构建一个多层感知器（MLP）神经网络和随机森林分类器。这里是一个简单的例子，首先确保已经安装了所需的库，如numpy, pandas, scikit-learn等：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 假设你已经有了一个名为df的数据集，其中包含特征列X和目标标签y
data = pd.read_csv('your_dataset.csv') # 替换为实际文件路径
X = data.drop('target', axis=1)  # 假设最后一列是目标变量
y = data['target']

# 数据预处理：分割数据、标准化
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 定义并训练MLP分类器
mlp = MLPClassifier(hidden_layer_sizes=(100, 50), activation='relu', max_iter=1000)
mlp.fit(X_train, y_train)

# 定义并训练随机森林分类器
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# 预测并评估性能
mlp_pred = mlp.predict(X_test)
rf_pred = rf.predict(X_test)

accuracy_mlp = accuracy_score(y_test, mlp_pred)
accuracy_rf = accuracy_score(y_test, rf_pred)

print(f"MLP分类器的准确率: {accuracy_mlp * 100:.2f}%")
print(f"随机森林分类器的准确率: {accuracy_rf * 100:.2f}%")

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