对于一个数据集，使用python语言，分别使用BPNN模型和SVM模型（参数自行设计）构建一个预测模型，并使用score（）函数评价模型准确度

在Python中，我们可以使用`sklearn`库来构建基于BP神经网络（Backpropagation Neural Network，简称BPNN）和支持向量机（Support Vector Machine，简称SVM）的预测模型。这里是一个基本的步骤概述：

1. **导入必要的库**:

   import numpy as np
   from sklearn.neural_network import MLPRegressor (for BPNN)
   from sklearn.svm import SVR
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import mean_squared_error, r2_score

2. **加载和预处理数据集**:

   data = ... # 加载数据集，例如从CSV文件或数据库读取
   X, y = data[:, :-1], data[:, -1]  # 分割特征和目标变量
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. **创建并训练BPNN模型**:

   bpnn = MLPRegressor(hidden_layer_sizes=(10, 10), activation='relu', solver='adam')  # 参数设置
   bpnn.fit(X_train, y_train)

4. **创建并训练SVM模型**:

   svm = SVR(kernel='rbf', C=1)  # 参数设置，如选择核函数和正则化参数C
   svm.fit(X_train, y_train)

5. **评估模型**:

   def score_model(model, X_test, y_test):
       y_pred = model.predict(X_test)
       mse_bpnn = mean_squared_error(y_test, y_pred)
       r2_bpnn = r2_score(y_test, y_pred)
       
       mse_svm = mean_squared_error(y_test, svm.predict(X_test))
       r2_svm = r2_score(y_test, svm.predict(X_test))
       
       return mse_bpnn, r2_bpnn, mse_svm, r2_svm

   bpnn_mse, bpnn_r2, svm_mse, svm_r2 = score_model(bpnn, X_test, y_test), score_model(svm, X_test, y_test)

6. **打印得分**:

   print(f"BPNN MSE: {bpnn_mse}, R^2: {bpnn_r2}")
   print(f"SVM MSE: {svm_mse}, R^2: {svm_r2}")