diff --git a/model_selection/cv_metric_gen.py b/model_selection/cv_metric_gen.py
index e8be09959b6593e9049e8abdae0a99780d45f061..7546601a3558c6dd78a3dbf83d82d4e11987946c 100644
--- a/model_selection/cv_metric_gen.py
+++ b/model_selection/cv_metric_gen.py
@@ -209,9 +209,8 @@ if __name__ == "__main__":
                 # Curve generation setup
                 mean_fpr = np.linspace(0, 1, 100)
                 tprs, aucs = [], []
-                recall_points = np.linspace(0, 1, 100)
-                all_precisions = []
-                pr_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()}
@@ -241,14 +240,18 @@ if __name__ == "__main__":
                     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))
+                    # Reverse the recall and precision arrays for interpolation
+                    recall_for_interp = pr_display.recall[::-1]
+                    precision_for_interp = pr_display.precision[::-1]
                     
-                    precision, recall = pr_display.precision, pr_display.recall
-                    pr_aucs.append(pr_display.average_precision)
-                    axes[model_idx][1].plot(recall, precision, alpha=0.6, lw=2, label=f"PR fold {fold_idx} (AP = {pr_display.average_precision:.2f})", color=cmap(fold_idx % 10))
+                    # Handle the edge case where recall_for_interp has duplicates, which can break np.interp
+                    recall_for_interp, unique_indices = np.unique(recall_for_interp, return_index=True)
+                    precision_for_interp = precision_for_interp[unique_indices]
                     
-                    # Store the precision values for each recall point
-                    all_precisions.append(np.interp(recall_points, recall[::-1], precision[::-1]))
-
+                    # Interpolate precision
+                    interp_precision = np.interp(mean_recall, recall_for_interp, precision_for_interp)
+                    precisions.append(interp_precision)
+                    pr_aucs.append(pr_display.average_precision)
                 # 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
@@ -259,11 +262,11 @@ if __name__ == "__main__":
                 # Set ROC plot limits and title
                 axes[model_idx][0].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"ROC Curve - {model_name} ({group}-{method_names[j]})")
                 axes[model_idx][0].legend(loc="lower right")
-
+                
                 # Compute mean Precision-Recall curve
-                mean_precision = np.mean(all_precisions, axis=0)
+                mean_precision = np.mean(precisions, axis=0)
                 mean_pr_auc = np.mean(pr_aucs)
-                axes[model_idx][1].plot(recall_points, mean_precision, color='b', lw=4, label=r'Mean PR (AP = %0.2f)' % mean_pr_auc, alpha=.8)
+                axes[model_idx][1].plot(mean_recall, mean_precision, color='b', lw=4, label=r'Mean PR (AUC = %0.2f)' % mean_pr_auc, alpha=.8)
                 # Plot baseline precision (proportion of positive samples)
                 baseline = np.sum(y_train) / len(y_train)
                 axes[model_idx][1].plot([0, 1], [baseline, baseline], linestyle='--', lw=2, color='r', alpha=.8, label='Baseline')