线性回归

二维线性回归（作了可视化处理）：

from sklearn.linear_model import LinearRegression
import numpy as np
import matplotlib.pyplot as plt
X=[[1],[4],[3]]
y=[3,5,3]
lr=LinearRegression()
model=lr.fit(X,y)
z=np.linspace(0,5,20)
plt.scatter(X,y,s=80)
plt.plot(z,model.predict(z.reshape(-1,1)),c='k')
plt.title('Linear Regression')
print("y={:.3f}x".format(model.coef_[0])+'+{:.3f}'.format(model.intercept_))
plt.show()

from sklearn.datasets import make_regression
X,y=make_regression(n_samples=50,n_features=1,n_informative=1,noise=50,random_state=1)
reg=LinearRegression()
reg.fit(X,y)
z=np.linspace(-3,3,200).reshape(-1,1)
plt.scatter(X,y,c='b',s=60)
plt.plot(z,reg.predict(z),c='k')
plt.title("Linear Regression2")
print("y={:.3f}x".format(reg.coef_[0])+'+{:.3f}'.format(reg.intercept_))
plt.show()

多维线性回归（分别使用了自造回归数据和糖尿病人的数据集）：

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
X,y=make_regression(n_samples=100,n_features=2,n_informative=2,random_state=38)
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)
lr=LinearRegression().fit(X_train,y_train)
print("the coefficient:{}".format(lr.coef_))
print('the intercept:{}'.format(lr.intercept_))
print("the score of this model:{:.3f}".format(lr.score(X_test,y_test)))

from sklearn.datasets import load_diabetes
X,y=load_diabetes().data,load_diabetes().target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)
lr=LinearRegression().fit(X_train,y_train)
print("the coefficient:{}".format(lr.coef_))
print('the intercept:{}'.format(lr.intercept_))
print("the score of this model:{:.3f}".format(lr.score(X_test,y_test)))