How to get a regression summary in scikit-learn like R does

0 votes

As an R user, I wanted to also get up to speed on scikit.

Creating a linear regression model(s) is fine, but can't seem to find a reasonable way to get a standard summary of regression output.

Code example:

# Linear Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LinearRegression

# Load the diabetes datasets
dataset = datasets.load_diabetes()

# Fit a linear regression model to the data
model = LinearRegression(),

# Make predictions
expected =
predicted = model.predict(

# Summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print model.intercept_, model.coef_, mse, 


  • seems like the intercept and coef are built into the model, and I just type print (second to last line) to see them.
  • What about all the other standard regression output like R^2, adjusted R^2, p values, etc. If I read the examples correctly, seems like you have to write a function/equation for each of these and then print it.
  • So, is there no standard summary output for lin. reg. models?
  • Also, in my printed array of outputs of coefficients, there are no variable names associated with each of these? I just get the numeric array. Is there a way to print these where I get an output of the coefficients and the variable they go with?

My printed output:

LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
152.133484163 [ -10.01219782 -239.81908937  519.83978679  324.39042769 -792.18416163
  476.74583782  101.04457032  177.06417623  751.27932109   67.62538639] 2859.69039877

Feb 25 in Power BI by surbhi
• 3,810 points

1 answer to this question.

0 votes
In sklearn, there is no summary report for R type regressions. The key reason for this is because sklearn is used for predictive modelling / machine learning, and the assessment criteria are based on performance on previously unseen data (for example, predictive r^2 for regression).

sklearn.metrics.classification_report is a summary function for classification that calculates several types of (predictive) scores on a classification model.
answered Feb 27 by CoolCoder
• 4,360 points

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