Binary classification is dichotomization applied to practical purposes, and in many practical binary classification problems, the two 2 groups are not symmetric – rather than overall accuracy, the relative proportion of different types of errors is of interest. For example, in medical testing, a false positive (detecting a disease when it is not present) is considered differently from a false negative (not detecting a disease when it is present).