diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index 654870bb55659a6a0e94a107a55a8b9cc3eaaf37..621ee28050d0e075ff26cc18b83442993d5026cc 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -255,7 +255,7 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
                 self.cv_results_["mean_test_score"].append(
                     cross_validation_score)
                 results[candidate_param_idx] = cross_validation_score
-                if cross_validation_score >= min(results.values()):
+                if cross_validation_score >= max(results.values()):
                     self.best_params_ = candidate_params[candidate_param_idx]
                     self.best_score_ = cross_validation_score
             except:
@@ -269,9 +269,6 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
                 'No fits were performed. All HP combination returned errors \n\n' + '\n'.join(
                     tracebacks))
         self.cv_results_["mean_test_score"] = np.array(self.cv_results_["mean_test_score"])
-        # for key, value in self.cv_results_.items():
-        #     if key.startswith("param_"):
-        #         self.cv_results_[key] = np.ma.array(data=value, mask=[False for _ in value])
         if self.refit:
             self.best_estimator_ = clone(base_estimator).set_params(
                 **self.best_params_)