(self, problem_type)
return self.__learners[problem_type]
Get the learner for a given problem type.

        Parameters
        ----------
        problem_type: str or Table or Domain
            If str, one of ``'classification'`` or ``'regression'``. If Table
            or Domain, the type is inferred from Domain's first class variable.

        Returns
        -------
        Learner
            The appropriate learner for the given problem type.

        Raises
        ------
        TypeError
            When (inferred) problem type not one of ``'classification'``
            or ``'regression'``.
Get the learner for a given problem type.
Get the learner for a given problem type .
def get_learner(self, problem_type):
        """Get the learner for a given problem type.

        Parameters
        ----------
        problem_type: str or Table or Domain
            If str, one of ``'classification'`` or ``'regression'``. If Table
            or Domain, the type is inferred from Domain's first class variable.

        Returns
        -------
        Learner
            The appropriate learner for the given problem type.

        Raises
        ------
        TypeError
            When (inferred) problem type not one of ``'classification'``
            or ``'regression'``.
        """
        if isinstance(problem_type, Table):
            problem_type = problem_type.domain
        if isinstance(problem_type, Domain):
            problem_type = (self.CLASSIFICATION if problem_type.has_discrete_class else
                            self.REGRESSION if problem_type.has_continuous_class else
                            None)
        # Prevent trying to access the learner when problem type is None
        if problem_type not in self.__fits__:
            raise TypeError("No learner to handle '{}'".format(problem_type))
        if self.__learners[problem_type] is None:
            learner = self.__fits__[problem_type](**self.__kwargs(problem_type))
            learner.use_default_preprocessors = self.use_default_preprocessors
            self.__learners[problem_type] = learner
        return self.__learners[problem_type]
def get_learner ( self , problem_type ) : if isinstance ( problem_type , Table ) : problem_type = problem_type . domain if isinstance ( problem_type , Domain ) : problem_type = ( self . CLASSIFICATION if problem_type . has_discrete_class else self . REGRESSION if problem_type . has_continuous_class else None ) # Prevent trying to access the learner when problem type is None if problem_type not in self . __fits__ : raise TypeError ( "No learner to handle '{}'" . format ( problem_type ) ) if self . __learners [ problem_type ] is None : learner = self . __fits__ [ problem_type ] ( * * self . __kwargs ( problem_type ) ) learner . use_default_preprocessors = self . use_default_preprocessors self . __learners [ problem_type ] = learner return self . __learners [ problem_type ]