statsmodels.discrete.conditional_models.ConditionalMNLogit.fit_regularized¶
- 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:
A results instance.
- Return type:
