二维线性回归(作了可视化处理): 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