Ordinal regression

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Regression analysis
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Background

In statistics, ordinal regression, also called ordinal classification, 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. It can be considered an intermediate problem between regression and classification.[1][2] Examples of ordinal regression are ordered logit and ordered probit. 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. In machine learning, ordinal regression may also be called ranking learning.[3][a]

  1. ^ Winship, Christopher; Mare, Robert D. (1984). "Regression Models with Ordinal Variables" (PDF). American Sociological Review. 49 (4): 512–525. doi:10.2307/2095465. JSTOR 2095465.
  2. ^ Gutiérrez, P. A.; Pérez-Ortiz, M.; Sánchez-Monedero, J.; Fernández-Navarro, F.; Hervás-Martínez, C. (January 2016). "Ordinal Regression Methods: Survey and Experimental Study". IEEE Transactions on Knowledge and Data Engineering. 28 (1): 127–146. doi:10.1109/TKDE.2015.2457911. hdl:10396/14494. ISSN 1041-4347.
  3. ^ Shashua, Amnon; Levin, Anat (2002). Ranking with large margin principle: Two approaches. NIPS.


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