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:¶
A results instance.
- Return type:¶