statsmodels.gam.generalized_additive_model.GLMGam.fit_regularized¶

GLMGam.
fit_regularized
(method='elastic_net', alpha=0.0, start_params=None, refit=False, opt_method='bfgs', **kwargs)¶ Return a regularized fit to a linear regression model.
 Parameters
 method{‘elastic_net’}
Only the elastic_net approach is currently implemented.
 alphascalar 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_paramsarray_like
Starting values for params.
 refitbool
If True, the model is refit using only the variables that have nonzero coefficients in the regularized fit. The refitted model is not regularized.
 opt_method
str
The method used for numerical optimization.
 **kwargs
Additional keyword arguments used when fitting the model.
 Returns
GLMResults
An array or a GLMResults object, 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:
\[loglike/n + alpha*((1L1\_wt)*params_2^2/2 + L1\_wt*params_1)\]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:
 maxiterint
Maximum number of iterations
 L1_wtfloat
Must be in [0, 1]. The L1 penalty has weight L1_wt and the L2 penalty has weight 1  L1_wt.
 cnvrg_tolfloat
Convergence threshold for line searches
 zero_tolfloat
Coefficients below this threshold are treated as zero.