diff --git a/model_selection/test_models.py b/model_selection/test_models.py
index 2624beb6d37791f47b9d48e1f97a02fb5c92d978..8f693c1b506982866169104607de6cf8ddb9dfcd 100644
--- a/model_selection/test_models.py
+++ b/model_selection/test_models.py
@@ -43,48 +43,48 @@ def get_tuned_models(group_id, method_id):
         # 1.1) Trained with original dataset
         if method_id == 0:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
-            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 117}), 
-            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 23, 'warm_start': True}),
-            "AB" : AdaBoostClassifier(**{'learning_rate': 1.9189147333140566, 'n_estimators': 131, 'algorithm': 'SAMME'}), 
-            "XGB": XGBClassifier(**{'learning_rate': 0.22870029177880222, 'max_depth': 8, 'n_estimators': 909}),
-            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
-            #"SVM" : SVC(**{'C': 0.9872682949695772, 'kernel': 'linear', 'max_iter':1000, 'probability': True}), 
-            "MLP" : MLPClassifier(**{'activation': 'identity', 'hidden_layer_sizes': 122, 'learning_rate': 'invscaling', 'max_iter':500})
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'entropy'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 123}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 13, 'warm_start': False}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.8473150336970519, 'n_estimators': 96, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.21528982071549305, 'max_depth': 6, 'n_estimators': 804}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2','max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.051871311397777, 'kernel': 'linear', 'max_iter':1000, 'probability': True}), 
+            "MLP" : MLPClassifier(**{'activation': 'identity', 'hidden_layer_sizes': 78, 'learning_rate': 'constant','max_iter':500})
             }
         # 1.2) 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', 'probability': True}),
+            "RF": RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 238, 'class_weight': 'balanced'}),
+            "Bagging": BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 22, 'warm_start': False, 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            "AB": AdaBoostClassifier(**{'learning_rate': 1.7136783954287846, 'n_estimators': 99, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
+            "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000, 'class_weight': 'balanced'}),
+            "SVM": SVC(**{'C': 1.480857958217729, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced', 'probability': True}),
             }
         # 1.3) 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}),
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 121}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 22, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.4640913091426446, 'n_estimators': 145, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.19621698151985992, 'max_depth': 7, 'n_estimators': 840}),
             "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
-            #"SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000, 'probability': True}), 
-            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
+            "SVM" : SVC(**{'C': 1.590799972846728, 'kernel': 'poly', 'max_iter':1000, 'probability': True}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 112, 'learning_rate': 'constant', 'max_iter':500})
             }
         # 1.4) Trained with undersampled training dataset
         elif method_id == 3:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
-            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 104}), 
-            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 38, 'warm_start': True}),
-            "AB" : AdaBoostClassifier(**{'learning_rate': 1.6996764264041269, 'n_estimators': 93, 'algorithm': 'SAMME'}), 
-            "XGB": XGBClassifier(**{'learning_rate': 0.26480707899668926, 'max_depth': 7, 'n_estimators': 959}),
-            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
-            #"SVM" : SVC(**{'C': 1.1996501173654208, 'kernel': 'poly', 'max_iter':1000, 'probability': True}), 
-            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 131, 'learning_rate': 'constant', 'max_iter':500})
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 148}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 24, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.7970533619575801, 'n_estimators': 122, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.13148624656904934, 'max_depth': 9, 'n_estimators': 723}),
+            "LR" : LogisticRegression(**{'solver': 'sag', 'penalty': 'l2', 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.383651513577477, 'kernel': 'poly', 'max_iter':1000, 'probability': True}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 89, 'learning_rate': 'invscaling', 'max_iter':500})
             }
     # 2. POST
     else: