How to get a regression summary in Python scikit like R does?

There exists no R type regression summary report in sklearn. The main reason is that sklearn is used for predictive modelling / machine learning and the evaluation criteria are based on performance on previously unseen data (such as predictive r^2 for regression).

There does exist a summary function for classification called sklearn.metrics.classification_report which calculates several types of (predictive) scores on a classification model.

For a more classic statistical approach, take a look at statsmodels.

I use:

import sklearn.metrics as metrics
def regression_results(y_true, y_pred):

    # Regression metrics
    explained_variance=metrics.explained_variance_score(y_true, y_pred)
    mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) 
    mse=metrics.mean_squared_error(y_true, y_pred) 
    mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)
    median_absolute_error=metrics.median_absolute_error(y_true, y_pred)
    r2=metrics.r2_score(y_true, y_pred)

    print('explained_variance: ', round(explained_variance,4))    
    print('mean_squared_log_error: ', round(mean_squared_log_error,4))
    print('r2: ', round(r2,4))
    print('MAE: ', round(mean_absolute_error,4))
    print('MSE: ', round(mse,4))
    print('RMSE: ', round(np.sqrt(mse),4))

statsmodels package gives a quiet decent summary

from statsmodels.api import OLS
OLS(dataset.target,dataset.data).fit().summary()