分别用普通线性回归、岭回归、Lasso回归对boston房价进行预测，比较效果

1.导入模块

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

#机器学习的普通线性模型、岭回归模型、lasso模型
from sklearn.linear_model import LinearRegression,Ridge,Lasso
#模型效果评估
from sklearn.metrics import r2_score
#导入机器学习相关的数据集
import sklearn.datasets as datasets

2.获取训练数据

#从datasets模块中导入boston房价数据
boston = datasets.load_boston()
data = boston.data
target=boston.target
#训练数据
X_train = data[:480]
Y_train = target[:480]
#测试数据
x_test = data[480:]
y_true = target[480:]

3.确定机器学习模型

line = LinearRegression()
ridge = Ridge()
lasso = Lasso()

4.训练数据

line.fit(X_train,Y_train)
ridge.fit(X_train,Y_train)
lasso.fit(X_train,Y_train)

5.预测数据

line_y_pre=line.predict(x_test)
ridge_y_pre=ridge.predict(x_test)
lasso_y_pre=lasso.predict(x_test)

6.绘制图像

plt.plot(y_true,label='True')
plt.plot(line_y_pre,label='Line')
plt.plot(ridge_y_pre,label='Ridge')
plt.plot(lasso_y_pre,label='Lasso')
plt.legend()

7.比较各个模型准确度评分

line_score=r2_score(y_true,line_y_pre)
ridge_score=r2_score(y_true,ridge_y_pre)
lasso_score=r2_score(y_true,lasso_y_pre)
display(line_score,ridge_score,lasso_score)