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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2014
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888391/ https://www.ncbi.nlm.nih.gov/pubmed/24427282 http://dx.doi.org/10.1371/journal.pone.0084217 |