GLM.fit_regularized(method='elastic_net', alpha=0.0, start_params=None, refit=False, **kwargs)
[source]
Return a regularized fit to a linear regression model.
Parameters: 


Returns:  
Return type: 
An array, or a GLMResults object of the same type returned by 
The penalty is the elastic net
penalty, which is a combination of L1 and L2 penalties.
The function that is minimized is:
where \(*_1\) and \(*_2\) are the L1 and L2 norms.
Postestimation results are based on the same data used to select variables, hence may be subject to overfitting biases.
The elastic_net method uses the following keyword arguments:
maxiter : int
L1_wt : float
cnvrg_tol : float
zero_tol : float
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© 2006 Jonathan E. Taylor
Licensed under the 3clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.genmod.generalized_linear_model.GLM.fit_regularized.html