Logistic回归模型系数

问题描述

我尝试对糖尿病进行Logistic回归并获得模型的结果，我假设每个变量都有1个系数数，但结果给了我3个不同的coef数列表以及3个不同的截距。我在线性回归中尝试过，每个线性回归都给出1

import pandas as pd
import sklearn
from sklearn.linear_model import LogisticRegression
import numpy as np
from sklearn import linear_model,preprocessing
data = pd.read_csv ('diabetestype.csv',sep = ',')

le = preprocessing.LabelEncoder()
Age = list(data['Age']) #will take all buying to a list and transform into proper integer values 
BSf = list(data['BS Fast'])
BSp = list(data['BS pp'])
PR = list(data['plasma R'])
PF = list(data['plasma F'])
Hb = list(data['HbA1c'])
Type = le.fit_transform(list(data['Type']))

X = list(zip(Age,BSf,BSp,PR,PF,Hb))
y = list(Type)


x_train,x_test,y_train,y_test = sklearn.model_selection.train_test_split(X,y,test_size = 0.1)
# model = linear_model.LinearRegression()
model = LogisticRegression()
model.fit (x_train,y_train)
acc = model.score(x_test,y_test)
coef = model.coef_
inter = model.intercept_
prediction = model.predict(x_test)
for i in range (5):
    print ('predicted ',prediction[i],'variables  ',x_test[i],'actual',y_test[i])
print(acc)
print(coef,inter)

结果是--------

predicted  1 variables (2,9,14,6,10) actual 1
predicted  2 variables (33,7,8,8) actual 2
predicted  0 variables (19,4,3,2,0) actual 0
predicted  0 variables (7,15,5,3) actual 0
predicted  0 variables (16,0) actual 0
1.0
[[-0.02543341  0.3763792  -0.2116062  -1.36365511 -0.87416662 -1.8448327 ]
 [ 0.00940748 -1.12894486  1.50994009  1.1101098   1.23563738 -0.2574385 ]
 [ 0.01602593  0.75256566 -1.29833389  0.25354531 -0.36147076  2.1022712 ]] [ 28.79209663 -19.24933782  -9.54275881]
C:\Users\nk\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:764: ConvergenceWarning: lbfgs Failed to converge (status=1):
STOP: TOTAL NO. of IteraTIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)

解决方法

来自documentation：

coef_: ndarray of shape (1,n_features) or (n_classes,n_features) （与intercept_相同）

您有3个课程。

在此最小示例中，还具有3个类：

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
X,y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X,y)
clf.predict(X[:2,:])

clf.predict_proba(X[:2,:])


clf.score(X,y)

set(y)  # >>>{0,1,2} --> there are 3 classes



clf.coef_ # >>> array([[-0.41874027,0.96699274,-2.52102832,-1.08416599],#            [ 0.53123044,-0.31473365,-0.20002395,-0.94866082],#            [-0.11249017,-0.65225909,2.72105226,2.03282681]])

clf.coef_.shape # >>> (3,4)

clf.intercept_ # >>> array([  9.84028024,2.21683511,-12.05711535])

您需要能够辨别样本是否属于哪个类别。无论您要测试哪个类，结果都将在0或1之间。
例如。在coef_的第一行中，检查它是否属于1类...等。

coefficients logistic-regression python scikit-learn