statsmodels.gam.generalized_additive_model.GLMGam.fit_regularized¶

GLMGam.
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{‘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.
 **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.