Different coefficients: scikit-learn vs statsmodels (logistic regression)

Question

When running a logistic regression, the coefficients I get using statsmodels are correct (verified them with some course material). However, I am unable to get the same coefficients with sklearn. I've tried preprocessing the data to no avail. This is my code:

Statsmodels:

import statsmodels.api as sm

X_const = sm.add_constant(X)
model = sm.Logit(y, X_const)
results = model.fit()
print(results.summary())

The relevant output is:

                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const      -0.2382      3.983     -0.060      0.952      -8.045       7.569
a           2.0349      0.837      2.430      0.015       0.393       3.676
b           0.8077      0.823      0.981      0.327      -0.806       2.421
c           1.4572      0.768      1.897      0.058      -0.049       2.963
d          -0.0522      0.063     -0.828      0.407      -0.176       0.071
e_2         0.9157      1.082      0.846      0.397      -1.205       3.037
e_3         2.0080      1.052      1.909      0.056      -0.054       4.070

Scikit-learn (no preprocessing)

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
results = model.fit(X, y)
print(results.coef_)
print(results.intercept_)

The coefficients given are:

array([[ 1.29779008,  0.56524976,  0.97268593, -0.03762884,  0.33646097,
     0.98020901]])

And the intercept/constant given is:

array([ 0.0949539])

As you can see, regardless of which coefficient corresponds to which variable, the numbers given by sklearn don't match the correct ones from statsmodels. What am I missing? Thanks in advance!

Answer 1

Thanks to a kind soul on reddit, this was solved. To get the same coefficients, one has to negate the regularisation that sklearn applies to logistic regression by default:

model = LogisticRegression(C=1e8)

Where C according to the documentation is:

C : float, default: 1.0

Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.