statsmodels.discrete.conditional_models.ConditionalMNLogit.fit_regularized¶
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ConditionalMNLogit.
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.
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.
**kwargs
Additional keyword argument that are used when fitting the model.
Returns: Results
A results instance.