Return a regularized fit to a linear regression model.
Parameters:  method : string
maxiter : integer
alpha : scalar or arraylike
L1_wt : scalar
start_params : arraylike
cnvrg_tol : scalar
zero_tol : scalar


Returns:  A RegressionResults object, of the same type returned by : ``fit``. : 
Notes
The approach closely follows that implemented in the glmnet package in R. The penalty is the “elastic net” penalty, which is a convex combination of L1 and L2 penalties.
The function that is minimized is: ..math:
0.5*RSS/n + alpha*((1L1_wt)*params_2^2/2 + L1_wt*params_1)
where RSS is the usual regression sum of squares, n is the sample size, and and 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.
References
Friedman, Hastie, Tibshirani (2008). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 122 Feb 2010.