We often want to predict one of several outcomes.
A classifer is a predictor whose codomain is a finite set. In this case, we call the codomain the label set and we call its elements classes (or labels, categories). We call the prediction of a classifier on an input a classification.
If the set of labels has two elements, then we call the classifier a binary classifier (or two-way classifier, two-class classifier, boolean classifier). In the case that there are $k$ labels, we call the classifier a $k$-way classifier (or $k$-class classifier, multi-class classifier). The second term is meant to indicate, not that the classifier assigns to each point several classes, but that the classification decision is made between several classes.
Let $A$ be a set of inputs and let $B$ be a set of labels. Define $B = \set{0, 1}$ (or $\set{-1,1}$, {False, True}, {Negative, Positive}). Then $B$ is finite with two elements and $f: A \to B$ is a binary classifier with labels $0$ and $1$.
If the case $B = $ $\{$No, Maybe, Yes$\}$, we call $f: A \to B$ a three-way classifier. Other examples for $B$ include a list of languages, the set of English words in some dictionary, or the set of $m!$ possible orders of $m$ horses in a race.
When dealing with a finite set of $k$ arbitrary objects, it is often convenient to take associate the objects with the first $k$ positive integers and take the set $B = \set{1, \dots , k}$ as the set of labels; here $k \in \N $.
Following our terminology, but speaking of processes, some authors refer to the application of inductors for these special cases as binary classification and multi-class classification. Or they speak of classification and classification problems. Roughly speaking, a classifier classifies all inputs into categories.
Alternatively, some authors (especially in the statistics literature) refer to a classifier as a discriminator and reference discrimination problems.