Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference (on a scale from, say, 1–5 for "very poor" through "excellent"), as well as in information retrieval.
[3][a] Ordinal regression can be performed using a generalized linear model (GLM) that fits both a coefficient vector and a set of thresholds to a dataset.
This set of thresholds divides the real number line into K disjoint segments, corresponding to the K response levels.
[4] The probit version of the above model can be justified by assuming the existence of a real-valued latent variable (unobserved quantity) y*, determined by[5] where ε is normally distributed with zero mean and unit variance, conditioned on x.
[7] Other methods rely on the principle of large-margin learning that also underlies support vector machines.