Difference between revisions of "Ordinal Regression"
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(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. [http://en.wikipedia.org/wiki/Ordinal_regression Wikipedia] | (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. [http://en.wikipedia.org/wiki/Ordinal_regression Wikipedia] | ||
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| + | Some examples of ranked values: | ||
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| + | * 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 | ||
http://i.stack.imgur.com/2qVDg.jpg | http://i.stack.imgur.com/2qVDg.jpg | ||
Revision as of 22:28, 31 May 2018
(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
