# statsmodels.discrete.conditional_models.ConditionalLogit.fit_regularized¶

method

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 :

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.

Returns
An array of parameter estimates.