Regressions Logistique

# -*- coding: utf-8 -*-
'''
Created on Wed Jan 16 10:23:46 2019

@author: K
https://pythonfordatascience.org/logistic-regression-python/
'''
# module imports
from patsy import dmatrices
import pandas as pd
from sklearn.linear_model import LogisticRegression
import statsmodels.discrete.discrete_model as sm

# read in the data & create matrices
df = pd.read_csv(r"C:\Users\K\Desktop\AI\Modèles\RegressionsLogistique\binary.csv", engine='python')
y, X = dmatrices('admit ~ gre + gpa + rank', df, return_type = 'dataframe')

# sklearn output
#model = LogisticRegression(fit_intercept = False,C=1e9)
#C=1/λ. où λ est le paramètre de régularisation
#model = LogisticRegression(fit_intercept=False, C=1e9, solver='newton-cg')
model = LogisticRegression(fit_intercept=False,C=1e9, solver='newton-cg')
mdl = model.fit(X, y)
print(model.coef_)

Xnew = [[1,800,4,1]]
ynew = mdl.predict(Xnew)
print(ynew)
print("*****************")
# sm
logit = sm.Logit(y, X)
print(logit.fit().params)