因变量 方差膨胀系数_多重共线性检验-方差膨胀系数（VIF）

import numpy as np

from sklearn.linear_model import LinearRegression

coef0=np.array([5,6,7,8,9,10,11,12])

X1=np.random.rand(100,8)

y=np.dot(X1,coef0)+np.random.normal(0,1.5,size=100)

training=np.random.choice([True,False],p=[0.8,0.2],size=100)

lr1=LinearRegression()

lr1.fit(X1[training],y[training])

# 系数的均方误差MSE

print(((lr1.coef_-coef0)**2).sum()/8)

# 测试集准确率(R2)

print(lr1.score(X1[~training],y[~training]))

X2=np.column_stack([X1,np.dot(X1[:,[0,1]],np.array([1,1]))+np.random.normal(0,0.05,size=100)])

X2=np.column_stack([X2,np.dot(X2[:,[1,2,3]],np.array([1,1,1]))+np.random.normal(0,0.05,size=100)])

X3=np.column_stack([X1,np.random.rand(100,2)])

import matplotlib.pyplot as plt

clf=LinearRegression()

vif2=np.zeros((10,1))

for i in range(10):

tmp=[k for k in range(10) if k!=i]

clf.fit(X2[:,tmp],X2[:,i])

vifi=1/(1-clf.score(X2[:,tmp],X2[:,i]))

vif2[i]=vifi

plt.figure()

ax = plt.gca()

ax.plot(vif2)

#ax.plot(vif3)

plt.xlabel('feature')

plt.ylabel('VIF')

plt.title('VIF coefficients of the features')

plt.axis('tight')

plt.show()