# Supervised Ordinal Prediction Algorithm

(Redirected from Ordinal regression)

A Supervised Ordinal Prediction Algorithm is a supervised prediction algorithm that can be applied by a supervised ordinal prediction system (to solve a supervised ordinal prediction task for training datasets with ordinal response variable).

**AKA:**Learning-to-Rank (LTR) Method, Ordinal Regression Method.**Context:**- It can range from being a Pointwise Ranking Algorithm to being a Pairwise Ranking Algorithm to being a Listwise Ranking Algorithm.

**Example(s):****Counter-Example(s):****See:**Ordinal Variable, Ordered Logit, Personalized Recommendation Method.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Ordinal_regression Retrieved:2015-1-10.
- In statistics,
**ordinal regression**is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. The two most common types of ordinal regression models are ordered logit, which applies to data that meet the proportional odds assumption, and ordered probit.

- In statistics,

### 2009

- (Liu, 2009) ⇒ Tie-Yan Liu. (2009). “Learning to Rank for Information Retrieval.” Foundations and Trends ® in Information Retrieval, 3(3).
- QUOTE: ... Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches.

### 2005

- (Liu & Agresti, 2005) ⇒ Ivy Liu, and Alan Agresti. (2005). “The Analysis of Ordered Categorical Data: An Overview and a Survey of Recent Developments.” Test 14, no. 1
- ABSTRACT: This article review methodologies used for analyzing ordered categorical (ordinal) response variables. We begin by surveying models for data with a single ordinal response variable. We also survey recently proposed strategies for modeling ordinal response variables when the data have some type of clustering or when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. Primary models in that case includemarginal models andcluster-specific (conditional) models for which effects apply conditionally at the cluster level. Related discussion refers to multi-level and transitional models. The main emphasis is on maximum likelihood inference, although we indicate certain models (e.g., marginal models, multi-level models) for which this can be computationally difficult. The Bayesian approach has also received considerable attention for categorical data in the past decade, and we survey recent Bayesian approaches to modeling ordinal response variables. Alternative, non-model-based, approaches are also available for certain types of inference.

### 1999

- (Herbrich et al., 1999) ⇒ Ralf Herbrich, Thore Graepel, and Klaus Obermayer. (1999). “Support Vector Learning for Ordinal Regression.” In: Proceedings of the Ninth International Conference on Artificial Neural Networks.
- QUOTE: We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. ...
... Problems of ordinal regression arise in many fields, e.g., in information retrieval (Herbrich et al. 1998), in econometric models (Tangian and Gruber 1995), and in classical statistics (McCullagh 1980; Anderson 1984).

- QUOTE: We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. ...