I'm pretty sure it's a feature, not a bug, but I would like to know if there is a way to make `sklearn` and `statsmodels` match in their logit estimates. A very simple example:

```
import numpy as np
import statsmodels.formula.api as sm
from sklearn.linear_model import LogisticRegression

np.random.seed(123)

n = 100
y = np.random.random_integers(0, 1, n)
x = np.random.random((n, 2))
# Constant term
x[:, 0] = 1.

```

The estimates with `statsmodels`:

```
sm_lgt = sm.Logit(y, x).fit()
    Optimization terminated successfully.
             Current function value: 0.675320
             Iterations 4
print sm_lgt.params
    [ 0.38442   -1.1429183]

```

And the estimates with `sklearn`:

```
sk_lgt = LogisticRegression(fit_intercept=False).fit(x, y)
print sk_lgt.coef_
    [[ 0.16546794 -0.72637982]]

```

I think it's got to do with the implementation in `sklearn`, which uses some sort of regularization. Is there an option to estimate a barebones logit as in `statsmodels` (it's substantially faster and scales much more nicely). Also, does `sklearn` provide inference (standard errors) or marginal effects?