(self, kwargs, problem_type)
return kwargs
Handle the kwargs to be passed to the learner before they are used.

        In some cases we need to manipulate the kwargs that will be passed to
        the learner, e.g. SGD takes a `loss` parameter in both the regression
        and classification learners, but the learner widget cannot
        differentiate between these two, so it passes classification and
        regression loss parameters individually. The appropriate one must be
        renamed into `loss` before passed to the actual learner instance. This
        is done here.
Handle the kwargs to be passed to the learner before they are used.
Handle the kwargs to be passed to the learner before they are used .
def _change_kwargs(self, kwargs, problem_type):
        """Handle the kwargs to be passed to the learner before they are used.

        In some cases we need to manipulate the kwargs that will be passed to
        the learner, e.g. SGD takes a `loss` parameter in both the regression
        and classification learners, but the learner widget cannot
        differentiate between these two, so it passes classification and
        regression loss parameters individually. The appropriate one must be
        renamed into `loss` before passed to the actual learner instance. This
        is done here.

        """
        return kwargs
def _change_kwargs ( self , kwargs , problem_type ) : return kwargs