An area under the ROC curve of 0.9486 indicates that the model has a high level of accuracy in distinguishing between classes. This suggests that the model has a strong ability to correctly classify positive and negative instances, with an overall high level of sensitivity and specificity. A high AUC value like 0.9486 suggests that the model has good predictive performance and is effective in making accurate predictions.
