diff --git a/model_selection/cv_metric_gen.py b/model_selection/cv_metric_gen.py
index 378551883cab4961ed66c5e5028a60086a188cd5..2042928a9e286f77500e901a1175c1209796fe29 100644
--- a/model_selection/cv_metric_gen.py
+++ b/model_selection/cv_metric_gen.py
@@ -191,23 +191,40 @@ if __name__ == "__main__":
             # Metric generation for each model
             for model_idx, (model_name, model) in enumerate(models.items()):
                 print(f"{group}-{method_names[j]}-{model_name}")
-                # Retrieve cv scores for our metrics of interest
-                scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
-                # Save results of each fold
-                for metric_name in scorings.keys():
-                    scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
-                # ---------------------------------------- Generate curves ----------------------------------------
+                # # Retrieve cv scores for our metrics of interest
+                # scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
+                # # Save results of each fold
+                # for metric_name in scorings.keys():
+                #     scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
                 mean_fpr = np.linspace(0, 1, 100)
                 tprs, aucs = [], []
                 mean_recall = np.linspace(0, 1, 100)
                 precisions, pr_aucs = [], []
                 cmap = plt.get_cmap('tab10')  # Colormap
+                # Initialize storage for scores for each fold
+                fold_scores = {metric_name: [] for metric_name in scorings.keys()}
                 # Loop through each fold in the cross-validation
-                for fold_idx, (train, test) in enumerate(cv.split(X_train, y_train)):
+                for fold_idx, (train_idx, test_idx) in enumerate(cv.split(X_train, y_train)):
+                    X_train_fold, X_test_fold = X_train[train_idx], X_train[test_idx]
+                    y_train_fold, y_test_fold = y_train[train_idx], y_train[test_idx]
                     # Fit the model on the training data
-                    model.fit(X_train[train], y_train[train])
+                    model.fit(X_train_fold, y_train_fold)
+                    # Predict on the test data
+                    if hasattr(model, "decision_function"):
+                        y_score = model.decision_function(X_test_fold)
+                    else:
+                        y_score = model.predict_proba(X_test_fold)[:, 1]  # Use probability of positive class
+                    y_pred = model.predict(X_test_fold)
+                    # Calculate and store the scores for each metric
+                    for metric_name, scorer in scorings.items():
+                        if metric_name in ['AUROC', 'AUPRC']:
+                            score = scorer._score_func(y_test_fold, y_score)
+                        else:
+                            score = scorer._score_func(y_test_fold, y_pred)
+                        fold_scores[metric_name].append(score)
+                    # --------------------- CURVES ---------------------------
                     # Generate ROC curve for the fold
-                    roc_display = RocCurveDisplay.from_estimator(model, X_train[test], y_train[test],
+                    roc_display = RocCurveDisplay.from_estimator(model, X_test_fold, y_test_fold,
                                                                 name=f"ROC fold {fold_idx}", alpha=0.6, lw=2,
                                                                 ax=axes[model_idx][0], color=cmap(fold_idx % 10))
                     interp_tpr = np.interp(mean_fpr, roc_display.fpr, roc_display.tpr)
@@ -215,12 +232,15 @@ if __name__ == "__main__":
                     tprs.append(interp_tpr)
                     aucs.append(roc_display.roc_auc)
                     # Generate Precision-Recall curve for the fold
-                    pr_display = PrecisionRecallDisplay.from_estimator(model, X_train[test], y_train[test],
+                    pr_display = PrecisionRecallDisplay.from_estimator(model, X_test_fold, y_test_fold,
                                                                     name=f"PR fold {fold_idx}", alpha=0.6, lw=2,
                                                                     ax=axes[model_idx][1], color=cmap(fold_idx % 10))
                     interp_precision = np.interp(mean_recall, pr_display.recall[::-1], pr_display.precision[::-1])
                     precisions.append(interp_precision)
                     pr_aucs.append(pr_display.average_precision)
+                # Store the fold scores in the dataframe
+                for metric_name, scores in fold_scores.items():
+                    scores_df.loc[f"{model_name}_{metric_name}"] = np.around(scores, 4)
                 # Plot diagonal line for random guessing in ROC curve
                 axes[model_idx][0].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8, label='Random guessing')
                 # Compute mean ROC curve
@@ -241,7 +261,6 @@ if __name__ == "__main__":
                 # Set Precision-Recall plot limits and title
                 axes[model_idx][1].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"Precision-Recall Curve - {model_name} ({group}-{method_names[j]})")
                 axes[model_idx][1].legend(loc="lower right")
-                # ---------------------------------------- End Generate Curves  ----------------------------------------
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df