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100% Classification Accuracy Considered Harmful: The Normalized Information Transfer Factor Explains the Accuracy Paradox

The most widely spread measure of performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy. Despite optimizing classification error rate, high accuracy models may fail to capture crucial informa...

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Библиографические подробности
Главные авторы: Valverde-Albacete, Francisco J., Peláez-Moreno, Carmen
Формат: Artigo
Язык:Inglês
Опубликовано: Public Library of Science 2014
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Online-ссылка:https://ncbi.nlm.nih.gov/pmc/articles/PMC3888391/
https://ncbi.nlm.nih.gov/pubmed/24427282
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0084217
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