Return a regularized fit to a linear regression model.
method : :
maxiter : integer
alpha : scalar or array-like
L1_wt : scalar
start_params : array-like
cnvrg_tol : scalar
zero_tol : scalar
A PHregResults object, of the same type returned by `fit`. :
The penalty is the”elastic net” penalty, which is a convex combination of L1 and L2 penalties.
The function that is minimized is: ..math:
-loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)
where and are the L1 and L2 norms.
Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases.