© 2021 IEEE.This study covers almost the ultimate set of binary-classification performance instruments derived from four dimensions of a confusion matrix, namely true positives/negatives and false positives/negatives and enhances their representation by establishing a meaningful interpretation of the dimensions. A common textual formatting scheme is provided to improve the readability and comprehensibility of performance instruments' representation. A compact dashboard (named 'TasKar', the abbreviation of 'Tasnif Karnesi', 'Classification Report' in Turkish) is developed and provided online to calculate and visualize a total of 52 performance instruments (27 measures, 23 metrics, and 2 indicators) by entering confusion matrix elements only. Taking parametric, variant, and recently proposed instruments the number covered becomes 65. Despite the limited approaches in confusion matrix visualization in the literature, three new graphics were devised to visualize true/false positive/negative rates (TPR, FPR, TNR, FNR), positive/negative predictive values (PPV, NPV), and false discovery/omission rates (FDR, FOR) performance metrics. It is expected that the proposed method and tool will be used by researchers in computation, interpretation, and standardized representation of classification performance as well as by teachers and students in machine learning education.