diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py
index bfd814119c92fb20bc7517359ce33d89eb7bd20f..ca31f54755e5da977c5d2f20e2cf1d133de94057 100644
--- a/model_selection/hyperparam_tuning.py
+++ b/model_selection/hyperparam_tuning.py
@@ -149,7 +149,7 @@ if __name__ == "__main__":
             # Use group of models with class weight if needed
             models = models_CS if j == 1 else models_simple 
             # 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'])
+            hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Best Parameters','Best Precision', 'Mean Precision', 'SD'])
             for model_name, model in models.items():
                 print(f"{group}-{method_names[j]}-{model_name}")
                 # Find optimal hyperparams for curr model
@@ -158,11 +158,18 @@ if __name__ == "__main__":
                 search.fit(X,y)
                 # Access the results
                 results = search.cv_results_
-                hyperparam_df.at[model_name,'Parameters']=search.best_params_
-                hyperparam_df.at[model_name,'Best Precision']=round(search.best_score_,4)
-                hyperparam_df.at[model_name,'Mean Precision']= np.mean(results['mean_test_score'])
-                hyperparam_df.at[model_name,'SD']= np.std(results['std_test_score'])
-            
+                # Best parameters and best score directly accessible
+                hyperparam_df.at[model_name, 'Best Parameters'] = search.best_params_
+                hyperparam_df.at[model_name, 'Best Precision'] = round(search.best_score_, 4)
+                # Finding the index for the best set of parameters
+                best_index = search.best_index_
+                # Accessing the mean and std of the test score specifically for the best parameters
+                mean_precision_best = results['mean_test_score'][best_index]
+                std_precision_best = results['std_test_score'][best_index]
+                # Storing these values
+                hyperparam_df.at[model_name, 'Mean Precision'] = mean_precision_best
+                hyperparam_df.at[model_name, 'SD'] = std_precision_best
+
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"
             sheets_dict[sheet_name] = hyperparam_df
diff --git a/model_selection/output_hyperparam/hyperparamers.xlsx b/model_selection/output_hyperparam/hyperparamers.xlsx
index 11584750562141b2d42534cde438f9f0cb40a299..598578e577cf2b89b361f358a0fcc87f12b68552 100644
Binary files a/model_selection/output_hyperparam/hyperparamers.xlsx and b/model_selection/output_hyperparam/hyperparamers.xlsx differ