模型构建

model = MLPRegressor(hidden_layer_sizes=(100,50,10), max_iter=1000, alpha=0.001, solver='lbfgs', verbose=0, random_state=21) model.fit(X_train, y_train)

预测结果

y_pred = model.predict(X_test)

模型评估

print('神经网络模型评估：') print('均方误差：%.2f' % mean_squared_error(y_test, y_pred)) print('R2得分：%.2f' % model.score(X_test, y_test))

模型构建

model = SVR(kernel='linear', C=1e3) model.fit(X_train, y_train)

预测结果

y_pred = model.predict(X_test)

模型评估

print('线性支持向量机模型评估：') print('均方误差：%.2f' % mean_squared_error(y_test, y_pred)) print('R2得分：%.2f' % model.score(X_test, y_test))

模型构建

model = SVR(kernel='poly', C=1e3, degree=2) model.fit(X_train, y_train)

预测结果

y_pred = model.predict(X_test)

模型评估

print('多项式支持向量机模型评估：') print('均方误差：%.2f' % mean_squared_error(y_test, y_pred)) print('R2得分：%.2f' % model.score(X_test, y_test)