title: Python scikit learn Linear Model Parameter Standard Error
body: <p>I am working with sklearn and specifically the linear_model module.  After fitting a simple linear as in</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  from sklearn import linear_model
  randn = np.random.randn
  
  X = pd.DataFrame(randn(10,3), columns=['X1','X2','X3'])
  y = pd.DataFrame(randn(10,1), columns=['Y'])        
  
  model = linear_model.LinearRegression()
  model.fit(X=X, y=y)
  </code></pre>
  
  <p>I see how I can access to coefficients and intercept via coef_ and intercept_, prediction is straightforward as well.  I would like to access a variance-covariance matrix for the parameters of this simple model, and the standard error of these parameters.  I am familiar with R and the vcov() function, and it seems that scipy.optimize has some functionality for this (<a href="https://stackoverflow.com/questions/14581358/getting-standard-errors-on-fitted-parameters-using-the-optimize-leastsq-method-i">Getting standard errors on fitted parameters using the optimize.leastsq method in python</a>) - does sklearn have any functionality for accessing these statistics??</p>
  
  <p>Appreciate any help on this.  </p>
  
  <p>-Ryan</p>