Regression summary in Python

I'd recommend taking a look at the statsmodels library. Sk-learn is great (and the other answers provide ways to get at R2 and other metrics), but statsmodels provides a regression summary very similar to the one you're probably used to in R.

As an example:

import statsmodels.api as sm
from sklearn.datasets import make_blobs

x, y = make_blobs(n_samples=50, n_features=2, cluster_std=5.0,
                  centers=[(0,0), (2,2)], shuffle=False, random_state=12)

logit_model = sm.Logit(y, sm.add_constant(x)).fit()
print logit_model.summary()

Optimization terminated successfully.
         Current function value: 0.620237
         Iterations 5
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                      y   No. Observations:                   50
Model:                          Logit   Df Residuals:                       47
Method:                           MLE   Df Model:                            2
Date:                Wed, 28 Dec 2016   Pseudo R-squ.:                  0.1052
Time:                        12:58:10   Log-Likelihood:                -31.012
converged:                       True   LL-Null:                       -34.657
                                        LLR p-value:                   0.02611
==============================================================================
                 coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
const         -0.0813      0.308     -0.264      0.792        -0.684     0.522
x1             0.1230      0.065      1.888      0.059        -0.005     0.251
x2             0.1104      0.060      1.827      0.068        -0.008     0.229
==============================================================================

If you want to add regularization, instead of calling .fit() after the Logit initialization you can call .fit_regularized() and pass in an alpha parameter (regularization strength). If you do this, remember that the C paramater in sk-learn is actually the inverse of regularization strength.

1. For obtaining of singificance levels you can use sklearn.feature_selection.f_regression.

2. For obtaining R2 you can use sklearn.metrics.r2_score

import statsmodels.api as sm      
x_train1 = sm.add_constant(x_train1)
lm_1 = sm.OLS(y_train, x_train1).fit()
lm_1.summary()

This is a very use full package for the once who are very much used to R's model summary

For more info refer below articles:

1. statsmodels.api
2. stats-models-vs-sklearn

You can call clf.score(test_samples, true_values) to get R².

Significance is not directly provided by sklearn but have at the answer here and this code.