def _run_search(self, evaluate_candidates):
        """Repeatedly calls `evaluate_candidates` to conduct a search.

        This method, implemented in sub-classes, makes it is possible to
        customize the the scheduling of evaluations: GridSearchCV and
        RandomizedSearchCV schedule evaluations for their whole parameter
        search space at once but other more sequential approaches are also
        possible: for instance is possible to iteratively schedule evaluations
        for new regions of the parameter search space based on previously
        collected evaluation results. This makes it possible to implement
        Bayesian optimization or more generally sequential model-based
        optimization by deriving from the BaseSearchCV abstract base class.

        Parameters
        ----------
        evaluate_candidates : callable
            This callback accepts a list of candidates, where each candidate is
            a dict of parameter settings. It returns a dict of all results so
            far, formatted like ``cv_results_``.

        Examples
        --------

        ::

            def _run_search(self, evaluate_candidates):
                'Try C=0.1 only if C=1 is better than C=10'
                all_results = evaluate_candidates([{'C': 1}, {'C': 10}])
                score = all_results['mean_test_score']
                if score[0] < score[1]:
                    evaluate_candidates([{'C': 0.1}])
        """