sklearn.linear_model.LogisticRegression(로지스틱 회귀)

import numpy as np
import matplotlib.pyplot as plt

 

# data 준비

from sklearn.datasets import make_blobs

X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3])

https://broscoding.tistory.com/128

머신러닝.datasets .make_blobs 사용하기

from sklearn.linear_model import LogisticRegression

 

# model 설정
model=LogisticRegression()
# 훈련
model.fit(X,y)
# 예측
pred_y=model.predict(X)

 

import mglearn

plt.figure(figsize=[8,6])

# 바탕 관련
mglearn.plots.plot_2d_classification(model,X,cm='Reds',alpha=0.3)

# scatter
mglearn.discrete_scatter(X[:,0],X[:,1],y)

https://broscoding.tistory.com/131

머신러닝.LogisticRegression.predict_proba