This paper provides a comprehensive overview of classification assessment measures, aiming to serve as a comprehensive source for researchers in the field. It begins by defining the confusion matrix for binary and multi-class classification problems, explaining how various classification metrics are derived from it. The paper discusses the influence of balanced and imbalanced data on these metrics and presents numerical examples to illustrate their calculation. Additionally, it introduces graphical assessment methods such as Receiver Operating Characteristic (ROC) curves, Precision-Recall (PR) curves, and Detection Error Trade-off (DET) curves, detailing their construction and interpretation. The paper also includes a step-by-step guide to plotting these curves and discusses the robustness of different metrics against imbalanced data. Finally, it presents results from a simple experiment and concludes with a discussion of the key findings. The paper is structured into eight sections, covering the basics of classification assessment, the ROC curve, the AUC metric, the PR curve, and other metrics like Youden's index and Matthews correlation coefficient. It emphasizes the importance of understanding these measures for evaluating classification models effectively.This paper provides a comprehensive overview of classification assessment measures, aiming to serve as a comprehensive source for researchers in the field. It begins by defining the confusion matrix for binary and multi-class classification problems, explaining how various classification metrics are derived from it. The paper discusses the influence of balanced and imbalanced data on these metrics and presents numerical examples to illustrate their calculation. Additionally, it introduces graphical assessment methods such as Receiver Operating Characteristic (ROC) curves, Precision-Recall (PR) curves, and Detection Error Trade-off (DET) curves, detailing their construction and interpretation. The paper also includes a step-by-step guide to plotting these curves and discusses the robustness of different metrics against imbalanced data. Finally, it presents results from a simple experiment and concludes with a discussion of the key findings. The paper is structured into eight sections, covering the basics of classification assessment, the ROC curve, the AUC metric, the PR curve, and other metrics like Youden's index and Matthews correlation coefficient. It emphasizes the importance of understanding these measures for evaluating classification models effectively.