机器学习模型问题

#划分train
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=1)

#构造Ridge的模型
from sklearn import linear_model
lassoRegression = linear_model.Ridge()
lassoRegression.fit(X_train, y_train)
print("权重向量:%s, b的值为:%.2f" % (lassoRegression.coef_, lassoRegression.intercept_))
print("损失函数的值:%.2f" % np.mean((lassoRegression.predict(X_test) - y_test) ** 2)）
print("预测性能得分: %.2f" % lassoRegression.score(X_test, y_test))


#构造Lasso的模型
ridgeRegression = linear_model.Lasso()
ridgeRegression.fit(X_train, y_train）
print("权重向量:%s, b的值为:%.2f" % (ridgeRegression.coef_, ridgeRegression.intercept_))
print("损失函数的值:%.2f" % np.mean((ridgeRegression.predict(X_test) - y_test) ** 2)）
print("预测性能得分: %.2f" % ridgeRegression.score(X_test, y_test))

 



#Lasso预测
y_pred = lassoRegression.predict(X_test)

#Ridge预测
y_pred = ridgeRegression.predict(X_test)



#mean_squared_error值
msn = np.sqrt(mean_squared_error(y_test, y_pred))
mean_squared_error越小越好