title: How to get a regression summary in scikit-learn like R does?
body: <p>As an R user, I wanted to also get up to speed on scikit. </p>
  
  <p>Creating a linear regression model(s) is fine, but can't seem to find a reasonable way to get a <strong>standard summary of regression output.</strong> </p>
  
  <p>Code example:</p>
  
  <pre><code># 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()
  model.fit(dataset.data, dataset.target)
  print(model)
  
  # Make predictions
  expected = dataset.target
  predicted = model.predict(dataset.data)
  
  # Summarize the fit of the model
  mse = np.mean((predicted-expected)**2)
  print model.intercept_, model.coef_, mse, 
  print(model.score(dataset.data, dataset.target))
  </code></pre>
  
  <p>Issues:</p>
  
  <ul>
  <li>seems like the <strong>intercept</strong> and <strong>coef</strong> are built into the model, and I just type <code>print</code> (second to last line) to see them.</li>
  <li>What about <strong>all the other standard regression output like R^2, adjusted R^2, p values, etc.</strong> If I read the examples correctly, seems like you have to write a function/equation for each of these and then print it. </li>
  <li>So, is there no standard summary output for lin. reg. models?</li>
  <li>Also, in my printed array of outputs of coefficients, <strong>there are no variable names associated with each of these? I just get the numeric array.</strong> Is there a way to print these where I get an output of the coefficients and the variable they go with? </li>
  </ul>
  
  <p>My printed output:</p>
  
  <pre><code>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
  0.517749425413
  </code></pre>
  
  <p>Notes: Started off with Linear, Ridge and Lasso. I have gone through the examples. Below is for the basic OLS. </p>