In computer science, a classifier can be a classification algorithm or a classifier, i.e. an algorithm that is used to categorize an input vector into a given set of classes.

The goal of machine learning is to define a model that performs well on a given dataset. The particular task of machine learning is often a classification problem, which is the problem of identifying to which of a set of categories (sub-populations) an observation, (or observations) belongs to. In many cases, the individual observations can be expressed as the vectors representing the values of the input variables of a given classifier. 

For example, an email can be represented as a vector where the elements represent the values of the email variable. Then, if the email variable is in the “spam” class, the email can be identified as an element of the “spam” vector. Otherwise, if the email variable is in the “non-spam” class, the email can be identified as an element of the “non-spam” vector. 

In a similar fashion, a diagnosis can be represented as a vector where the elements represent the values of the diagnosis variable. Then, if the diagnosis variable is in the “malignant” (cancer) class, the diagnosis can be identified as an element of the “cancer” vector. Otherwise, if the diagnosis variable is in the “benign” (non-cancer) class, the diagnosis can be identified as an element of the “non-cancer” vector.

When performing a classification that compares two or more classes, it is important to compare the classes by means of a similarity or similarity function. Typically, a similarity function is chosen so that the similarity between two classes is high if they are similar, but the similarity of two classes is low if they are dissimilar.