Regenerated with sd and mean for cv

model_selection/hyperparam_tuning.py

@@ -156,8 +156,12 @@ if __name__ == "__main__":
                 params = hyperparameters[model_name]
                 search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=10, scoring='precision')
                 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,'Score']=round(search.best_score_,4)
+                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'])
             
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"