from sklearn.datasets import make_classification
import numpy as np
from sklearn.model_selection import train_test_split
from matplotlib import pyplot as plt

X,y = make_classification(
    n_samples=200,
    n_features=2,
    n_redundant=0,
    n_informative=2,
)
x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)

from sklearn.linear_model import LogisticRegression
lrg = LogisticRegression()
lrg.fit(x_train, y_train)
lrg.score(x_test, y_test)

运行结果

 0.9 

plt.scatter(x_train[:,0],x_train[:,1],c=y_train)
plt.show()

lrg.coef_,lrg.intercept_

运行结果

 (array([[-0.77217001, 2.58603312]]), array([-0.10393696])) 

$w_1x_1 + w_2x_2 + b = 0$ 转化得到$x_2 = (-w_1x_1 - b)/w_2$

# 绘制决策边界
x1 = np.linspace(-4,4,1000)
x2 = (-lrg.coef_[0][0]*x1 - lrg.intercept_[0])/lrg.coef_[0][1]
plt.plot(x1,x2)
plt.scatter(X[:,0],X[:,1],c=y)
plt.show()