Difference between revisions of "Ordinal Regression"
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* [[Capabilities]] | * [[Capabilities]] | ||
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* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/ordinal-regression Ordinal Regression |] [[Microsoft]] | * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/ordinal-regression Ordinal Regression |] [[Microsoft]] | ||
* [http://statisticsbyjim.com/regression/choosing-regression-analysis/ Choosing the Correct Type of Regression Analysis | Jim Frost] | * [http://statisticsbyjim.com/regression/choosing-regression-analysis/ Choosing the Correct Type of Regression Analysis | Jim Frost] | ||
Revision as of 10:31, 4 June 2023
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- Ordinal Regression | Microsoft
- Choosing the Correct Type of Regression Analysis | Jim Frost
(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. 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. Wikipedia
Some examples of ranked values:
- Survey responses that capture user’s preferred brands on a 1 to 5 scale
- The order of finishers in a race
- URLs in ranked search results
