Typically, the performance of a classification model is assessed by calculating various indicators including accuracy, precision, recall, F1 score, Receiver Operating Characteristic (ROC) Curve, and Area Under Curve (AUC) based on four entries of the predictions made by a model in comparison to the actual labels of the data, i.e., true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). However, in the case of one-class classification, only the TP and FN can be obtained since only "True" actual values are available.