SUBSCRIBESUBSCRIBESUBSCRIBEHomeHistory & SocietyScience & TechBiographiesAnimals & NatureGeography & TravelArts & CultureProConMoneyGames & QuizzesVideosOn This DayOne Good FactDictionaryNew ArticlesHistory & SocietyLifestyles & Social IssuesPhilosophy & ReligionPolitics, Law & GovernmentWorld HistoryScience & TechHealth & MedicineScienceTechnologyBiographiesBrowse BiographiesAnimals & NatureBirds, Reptiles & Other VertebratesEnvironmentFossils & Geologic TimeInsects & Other InvertebratesMammalsPlantsGeography & TravelGeography & TravelArts & CultureEntertainment & Pop CultureLiteratureSports & RecreationVisual ArtsImage GalleriesPodcastsSummariesTop QuestionsLists and StoriesBritannica KidsAsk the ChatbotGames & QuizzesHistory & SocietyScience & TechBiographiesAnimals & NatureGeography & TravelArts & CultureProConMoneyVideoscross-validationIntroductionMotivations and mechanismsTypes of cross-validationReferences & Edit HistoryRelated TopicsTechnologyComputersFacebookXhttps://www.britannica.com/technology/cross-validation-computer-scienceMichael McDonough
Michael McDonough was a media team intern at Encyclopaedia Britannica. He is expected to graduate in 2023 from Northwestern University.Britannica Editors
Encyclopaedia Britannica's editors oversee subject areas in which they have extensive knowledge, whether from years of experience gained by working on that content or via study for an advanced degree....Historymachine learningencompassesalgorithmscomprisesparameterassessmentstatisticsiterationiterationsvarianceallocatescompositionexclusivemachine learningSee all related contentSUBSCRIBE
A less common method is leave-one-out cross-validation (LOOCV). A special variation of k-fold cross-validation, LOOCV occurs when the number of folds (k) is equal to the number of sample points (n). In each iteration of testing, a single data point will be left out and function as the assessment set. The process is repeated for all n observations, and the k results are averaged to produce the overall performance statistic. However, high computational cost and large variances almost always make LOOCV less useful than other types of cross-validation, except for datasets with a very small n. Michael McDonough
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