Binary classifiers are used to separate the elements of a given dataset into one of two possible groups (for example, fraud or not fraud) and are a special case of multiclass classification. Most binary classification metrics can be generalized to multiclass classification metrics. A multiclass classification describes a classification problem, where there are M>2 possible labels for each data point (the case where M=2 is the binary classification problem).

For multiclass metrics, the notion of positives and negatives is slightly different. Predictions and labels can still be positive or negative, but they must be considered in the context of a particular class. Each label and prediction takes on the value of one of the multiple classes and so they are said to be positive for their particular class and negative for all other classes. So, a true positive occurs whenever the prediction and the label match, while a true negative occurs when neither the prediction nor the label takes on the value of a given class. By this convention, there can be multiple true negatives for a given data sample. The extension of false negatives and false positives from the former definitions of positive and negative labels is straightforward.