diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py
index 22d004afff2dada2d85b9a4973fd5bbb97ce50c9..d90158ddd48c97137f30ccc0a297ec7878b42753 100644
--- a/model_selection/hyperparam_tuning.py
+++ b/model_selection/hyperparam_tuning.py
@@ -143,16 +143,17 @@ if __name__ == "__main__":
     # Store each df as a sheet in an excel file
     sheets_dict = {}
     for i, group in enumerate(['post']):
-        for j, method in enumerate(['', '', 'over_', 'under_']): 
+        for j, method in enumerate(['']): #['', '', 'over_', 'under_']
             # Get dataset based on group and method
             X = data_dic['X_train_' + method + group]
             y = data_dic['y_train_' + method + group]
             # Use group of models with class weight if needed
-            models = models_CS if j == 2 else models_simple 
+            # models = models_CS if j == 1 else models_simple 
+            models = models_CS
             # Save results: params and best score for each of the mdodels of this method and group
             hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Parameters','Score'])
             for model_name, model in models.items():
-                print(f"{group}-{method_names[j]}-{model_name}")
+                print(f"{group}-{method_names[1]}-{model_name}")
                 # Find optimal hyperparams for curr model
                 params = hyperparameters[model_name]
                 search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=8, scoring='precision')
@@ -161,11 +162,11 @@ if __name__ == "__main__":
                 hyperparam_df.at[model_name,'Score']=round(search.best_score_,4)
             
             # Store the DataFrame in the dictionary with a unique key for each sheet
-            sheet_name = f"{group}_{method_names[j]}"
+            sheet_name = f"{group}_{method_names[1]}"
             sheets_dict[sheet_name] = hyperparam_df
 
     # Write results to Excel file
-    with pd.ExcelWriter('./output/hyperparam_post.xlsx') as writer:
+    with pd.ExcelWriter('./output/hyperparam_post_ORIG_CS.xlsx') as writer:
         for sheet_name, data in sheets_dict.items():
             data.to_excel(writer, sheet_name=sheet_name)
 
diff --git a/model_selection/output/hyperparam_post.xlsx b/model_selection/output/hyperparam_post.xlsx
index 5d04496f151ed7200fe8a27aa23fcf6af97fbbdd..5d46a7a12470aac39458aeed7e1922243c547c25 100644
Binary files a/model_selection/output/hyperparam_post.xlsx and b/model_selection/output/hyperparam_post.xlsx differ
diff --git a/model_selection/output/hyperparam_post_ORIG_CS.xlsx b/model_selection/output/hyperparam_post_ORIG_CS.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..39de3ce34113aa5c0edfb8f025727cc199579451
Binary files /dev/null and b/model_selection/output/hyperparam_post_ORIG_CS.xlsx differ