statsmodels.regression.linear_model.GLS.fit_regularized¶

GLS.
fit_regularized
(method='elastic_net', alpha=0.0, L1_wt=1.0, start_params=None, profile_scale=False, refit=False, **kwargs)[source]¶ Return a regularized fit to a linear regression model.
Parameters:  method (string) – Only the ‘elastic_net’ approach is currently implemented.
 alpha (scalar or arraylike) – 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.
 L1_wt (scalar) – The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit.
 start_params (arraylike) – Starting values for
params
.  profile_scale (bool) – If True the penalized fit is computed using the profile (concentrated) loglikelihood for the Gaussian model. Otherwise the fit uses the residual sum of squares.
 refit (bool) – If True, the model is refit using only the variables that have nonzero coefficients in the regularized fit. The refitted model is not regularized.
 distributed (bool) – If True, the model uses distributed methods for fitting, will raise an error if True and partitions is None.
 generator (function) – generator used to partition the model, allows for handling of out of memory/parallel computing.
 partitions (scalar) – The number of partitions desired for the distributed estimation.
 threshold (scalar or arraylike) – The threshold below which coefficients are zeroed out, only used for distributed estimation
Returns: Return type: A RegularizedResults instance.
Notes
The elastic net approach closely follows that implemented in the glmnet package in R. The penalty is a combination of L1 and L2 penalties.
The function that is minimized is:
\[0.5*RSS/n + alpha*((1L1\_wt)*params_2^2/2 + L1\_wt*params_1)\]where RSS is the usual regression sum of squares, n is the sample size, and \(*_1\) and \(*_2\) are the L1 and L2 norms.
For WLS and GLS, the RSS is calculated using the whitened endog and exog data.
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
 Maximum number of iterations
 cnvrg_tol : float
 Convergence threshold for line searches
 zero_tol : float
 Coefficients below this threshold are treated as zero.
References
Friedman, Hastie, Tibshirani (2008). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 122 Feb 2010.