Binary classification is one of the most frequent studies in applied machine learning problems in various domains, from medicine to biology to meteorology to malware analysis. Many researchers use some performance metrics in their classification studies to report their success. However, the literature has shown a widespread confusion about the terminology and ignorance of the fundamental aspects behind metrics. This paper clarifies the confusing terminology, suggests formal rules to distinguish between measures and metrics for the first time, and proposes a new comprehensive visualized roadmap in a leveled structure for 22 measures and 22 metrics for exploring binary classification performance. Additionally, we introduced novel concepts such as canonical notation, duality, and complementation for measures/metrics, and suggested two new canonical base measures simplifying equations. It is expected that the study will guide other studies to have standardized approach to performance metrics for machine learning based solutions.