Removed sd shading

model_selection/cv_metric_gen.py

@@ -214,23 +214,14 @@ if __name__ == "__main__":
                         # Append the interpolated TPR and AUC for this fold
                         tprs.append(interp_tpr)
                         aucs.append(roc_display.roc_auc)
-                # Plot the diagonal line representing random guessing
-                axes[model_idx].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8)
-                # Compute the mean and standard deviation of the TPRs
+                # Compute the mean of the TPRs
                 mean_tpr = np.mean(tprs, axis=0)
                 mean_tpr[-1] = 1.0
                 mean_auc = auc(mean_fpr, mean_tpr)  # Calculate the mean AUC
-                std_auc = np.std(aucs)
                 # Plot the mean ROC curve
                 axes[model_idx].plot(mean_fpr, mean_tpr, color='b',
-                                    label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
+                                    label=r'Mean ROC (AUC = %0.2f)' % mean_auc,
                                     lw=2, alpha=.8)
-                # Plot the standard deviation of the TPRs
-                std_tpr = np.std(tprs, axis=0)
-                tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
-                tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
-                axes[model_idx].fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
-                                            label=r'$\pm$ 1 std. dev.')
                 # Set plot limits and title
                 axes[model_idx].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
                                     title=f"ROC Curve - {model_name} ({group}-{method_names[j]})")