diff --git a/model_selection/test_models.py b/model_selection/test_models.py
index 3004bf2acada251cf4d869feb6724b1811b7d137..191ae8308752b920a68816320f62007537901c1c 100644
--- a/model_selection/test_models.py
+++ b/model_selection/test_models.py
@@ -62,7 +62,6 @@ def get_tuned_models(group_id, method_id):
             "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'none', 'max_iter': 1000, 'class_weight': 'balanced'}),
             "SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced'}),
             }
-
         # 1.3) Trained with oversampled training dataset
         elif method_id == 2:
             tuned_models = {
@@ -103,13 +102,38 @@ def get_tuned_models(group_id, method_id):
             }
         # 2.2) Trained with original dataset and cost-sensitive learning
         elif method_id == 1:
-            ...
+            tuned_models = {
+            # "DT": DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'entropy', 'class_weight': 'balanced'}),
+            # "RF": RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 118, 'class_weight': 'balanced'}),
+            # "Bagging": BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'warm_start': False, 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            # "AB": AdaBoostClassifier(**{'learning_rate': 0.8159074545140872, 'n_estimators': 121, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            # "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'none', 'max_iter': 1000, 'class_weight': 'balanced'}),
+            # "SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced'}),
+            }
         # 2.3) Trained with oversampled training dataset
         elif method_id == 2:
-            ...
+            tuned_models = {
+            # "DT" : DecisionTreeClassifier(**{'splitter': 'random', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
+            # "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 135}), 
+            # "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 26, 'warm_start': True}),
+            # "AB" : AdaBoostClassifier(**{'learning_rate': 1.6590924545876917, 'n_estimators': 141, 'algorithm': 'SAMME'}), 
+            # "XGB": XGBClassifier(**{'learning_rate': 0.26946295284728783, 'max_depth': 7, 'n_estimators': 893}),
+            # "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
+            # "SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000}), 
+            # "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
+            }
         # 2.4) Trained with undersampled training dataset
         elif method_id == 3:
-            ...
+            tuned_models = {
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'entropy'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 224}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 13, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.836659462701278, 'n_estimators': 138, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.2517946893282251, 'max_depth': 4, 'n_estimators': 646}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.8414678085000697, 'kernel': 'linear', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 76, 'learning_rate': 'constant', 'max_iter':500})
+            }
     return tuned_models
 # --------------------------------------------------------------------------------------------------------