1.将线性回归得到的两个参数打印出来，并且在图像中画出该拟合曲线。 import matplotlib.pyplot as pltimport numpy as npimport pandas as pddata=pd.read_csv( "finalData.csv", thousands=',')data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))plt.axis([0, 60000, 0, 10])X=np.linspace(0, 60000, 1000)plt.plot(X, t0 + t1*X, "b")plt.text(5000, 3.1, r"$theta_0 = 4.85$", fontsize=14, color="b")plt.text(5000, 2.2, r"$theta_1 = 4.91 times 10^{-5}$", fontsize=14, color="b")plt.show()from sklearn import linear_modellin1 = linear_model.LinearRegression()Xsample = np.c_[data["GDP per capita"]]ysample = np.c_[data["Life satisfaction"]]lin1.fit(Xsample, ysample)# 输出 intercept_是截距, coef_是斜率系数t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]print('打印t0',t0)print('打印t1',t1) 2.结果展示
输出 intercept_是截距, coef_是斜率系数 ，这些也被成为回归系数。