diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py
index 907d6f71035ea310fb4cc98ee7a6986acc3654b5..7a76d5ce0e8e79c8f387cb051bfbb02cb4383301 100644
--- a/model_selection/hyperparam_tuning.py
+++ b/model_selection/hyperparam_tuning.py
@@ -72,22 +72,22 @@ if __name__ == "__main__":
     # --------------------------------------------------------------------------------------------------------
     # 1. No class weight
     models_simple = {"DT" : DecisionTreeClassifier(), 
-            # "RF" : RandomForestClassifier(), 
-            # "Bagging" : BaggingClassifier(),
-            # "AB" : AdaBoostClassifier(algorithm='SAMME'), 
-            # "XGB": XGBClassifier(),
-            # "LR" : LogisticRegression(max_iter=1000), 
-            # "SVM" : SVC(probability=True, max_iter=1000), 
-            # "MLP" : MLPClassifier(max_iter=500)
+            "RF" : RandomForestClassifier(), 
+            "Bagging" : BaggingClassifier(),
+            "AB" : AdaBoostClassifier(algorithm='SAMME'), 
+            "XGB": XGBClassifier(),
+            "LR" : LogisticRegression(max_iter=1000), 
+            "SVM" : SVC(probability=True, max_iter=1000), 
+            "MLP" : MLPClassifier(max_iter=500)
             }
     
     # 2. Class weight: cost-sensitive learning
     models_CS = {"DT" : DecisionTreeClassifier(class_weight='balanced'), 
-            # "RF" : RandomForestClassifier(class_weight='balanced'), 
-            # "Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),
-            # "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),  
-            # "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'), 
-            # "SVM" : SVC(probability=True, max_iter = 1000, class_weight='balanced'), 
+            "RF" : RandomForestClassifier(class_weight='balanced'), 
+            "Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),
+            "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),  
+            "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'), 
+            "SVM" : SVC(probability=True, max_iter = 1000, class_weight='balanced')
             }
     # --------------------------------------------------------------------------------------------------------