statsmodels.discrete.conditional_models.ConditionalLogit.fit_regularized¶

ConditionalLogit.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 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.