线性回归

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

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

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

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