diff --git a/model_selection/test_models.py b/model_selection/test_models.py
index 3eda93cc279d1c74b3ed507c8c3577c1a25fc118..3004bf2acada251cf4d869feb6724b1811b7d137 100644
--- a/model_selection/test_models.py
+++ b/model_selection/test_models.py
@@ -55,13 +55,14 @@ def get_tuned_models(group_id, method_id):
         # 1.2) Trained with original dataset and cost-sensitive learning
         elif method_id == 1:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'log_loss', '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}, estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),  
-            "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'), 
+            "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'}),
             }
+
         # 1.3) Trained with oversampled training dataset
         elif method_id == 2:
             tuned_models = {