Probabilistic classification: Difference between revisions

Revision as of 06:46, 25 April 2023

Revision as of 00:49, 8 June 2023

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{{Short description|Machine learning problem}}

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In [[machine learning]], a '''probabilistic classifier''' is a [[statistical classification|classifier]] that is able to predict, given an observation of an input, a [[probability distribution]] over a [[Set (mathematics)|set]] of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right<ref>{{cite book |first1=Trevor |last1=Hastie |first2=Robert |last2=Tibshirani |first3=Jerome |last3=Friedman |year=2009 |title=The Elements of Statistical Learning |url=http://statweb.stanford.edu/~tibs/ElemStatLearn/ |page=348 |quote=[I]n [[data mining]] applications the interest is often more in the class probabilities <math>p_\ell(x), \ell = 1, \dots, K</math> themselves, rather than in performing a class assignment. |url-status=dead |archive-url=https://web.archive.org/web/20150126123924/http://statweb.stanford.edu/~tibs/ElemStatLearn/ |archive-date=2015-01-26 }}</ref> or when combining classifiers into [[ensemble classifier|ensembles]].