问题
I'm working on a classification problem and need the coefficients of the logistic regression equation. I can find the coefficients in R but I need to submit the project in python. I couldn't find the code for learning coefficients of logistic regression in python. How to get the coefficient values in python?
回答1:
sklearn.linear_model.LogisticRegression is for you. See this example:
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X, y)
print(clf.coef_, clf.intercept_)
回答2:
The statsmodels library would give you a breakdown of the coefficient results, as well as the associated p-values to determine their significance.
Using an example of x1 and y1 variables:
x1_train, x1_test, y1_train, y1_test = train_test_split(x1, y1, random_state=0)
logreg = LogisticRegression().fit(x1_train,y1_train)
logreg
print("Training set score: {:.3f}".format(logreg.score(x1_train,y1_train)))
print("Test set score: {:.3f}".format(logreg.score(x1_test,y1_test)))
import statsmodels.api as sm
logit_model=sm.Logit(y1,x1)
result=logit_model.fit()
print(result.summary())
Example results:
Optimization terminated successfully.
Current function value: 0.596755
Iterations 7
Logit Regression Results
==============================================================================
Dep. Variable: IsCanceled No. Observations: 20000
Model: Logit Df Residuals: 19996
Method: MLE Df Model: 3
Date: Sat, 17 Aug 2019 Pseudo R-squ.: 0.1391
Time: 23:58:55 Log-Likelihood: -11935.
converged: True LL-Null: -13863.
LLR p-value: 0.000
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const -2.1417 0.050 -43.216 0.000 -2.239 -2.045
x1 0.0055 0.000 32.013 0.000 0.005 0.006
x2 0.0236 0.001 36.465 0.000 0.022 0.025
x3 2.1137 0.104 20.400 0.000 1.911 2.317
==============================================================================
回答3:
Have a look at the statsmodels library's Logit model.
You would use it like this:
from statsmodels.discrete.discrete_model import Logit
from statsmodels.tools import add_constant
x = [...] # Obesrvations
y = [...] # Response variable
x = add_constant(x)
print(Logit(y, x).fit().summary())
回答4:
Luffy, please remember to always share your code and your attempts so we can know what you tried and help you out. Regardless of that, I think you are looking for this:
import numpy as np
from sklearn.linear_model import LogisticRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) #Your x values, for a 2 variable model.
#y = 1 * x_0 + 2 * x_1 + 3 #This is the "true" model
y = np.dot(X, np.array([1, 2])) + 3 #Generating the true y-values
reg = LogisticRegression().fit(X, y) #Fitting the model given your X and y values.
reg.coef_ #Prints an array of all regressor values (b1 and b2, or as many bs as your model has)
reg.intercept_ #Prints value for intercept/b0
reg.predict(np.array([[3, 5]])) #Predicts an array of y-values with the fitted model given the inputs
回答5:
Provided that your X is a Pandas DataFrame and clf is your Logistic Regression Model you can get the name of the feature as well as its value with this line of code:
pd.DataFrame(zip(X_train.columns, np.transpose(clf.coef_)), columns=['features', 'coef'])
回答6:
a little correction last answer:
pd.DataFrame(zip(X_train.columns, np.transpose(clf.coef_.tolist()[0])), columns=['features', 'coef'])
来源：https://stackoverflow.com/questions/57924484/finding-coefficients-for-logistic-regression-in-python