statsmodels.genmod.generalized_linear_model.GLM.fit_regularized

GLM.fit_regularized(method='elastic_net', alpha=0.0, start_params=None, refit=False, **kwargs)

Return a regularized fit to a linear regression model.

Parameters:

method :

Only the elastic_net approach is currently implemented.

alpha : scalar or array-like

The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as params, and contains a penalty weight for each coefficient.

start_params : array-like

Starting values for params.

refit : bool

If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized.

Returns:

An array, or a GLMResults object of the same type returned by fit.

Notes

The penalty is the elastic net penalty, which is a 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.

The elastic_net method uses the following keyword arguments:

maxiter : int

Maximum number of iterations

L1_wt : float

Must be in [0, 1]. The L1 penalty has weight L1_wt and the L2 penalty has weight 1 - L1_wt.

cnvrg_tol : float

Convergence threshold for line searches

zero_tol : float

Coefficients below this threshold are treated as zero.