LR共线性问题样例

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import datasets

iris = datasets.load_iris()
X = np.array([
        [0,1,1,1],
        [0,1,1,1],
        [0,1,1,0],
        [0,1,1,1],
        [0,1,1,1],
        [0,1,0,0],
        [0,1,0,0],
        [0,0,0,0],
        [0,0,1,0],
        [0,1,0,0],
    ])
y = np.array([1,1,1,1,1,0,0,0,0,0])
lr = LogisticRegression(C=1, penalty='l2',intercept_scaling=0.01)
# lr = LogisticRegression(C=1, penalty='l2')
lr.fit(X, y)
print(lr.intercept_,lr.coef_)
# [-0.00012842] [[ 0.         -0.14693214  0.59779321  0.85362485]]

# 1、正样本为0,负样本为1.
# 2、第二列特征，命中特征的样本数8个，正样本3个，负样本5个，负样本大于正样本，从单个特征看，应该对结果1产生支持，也就是weight应该大于0。 
# 但，算出来的权值：-0.14693214 负的，对0预测产生支持。