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Bias-variance decomposition of absolute errors for diagnosing regression models of continuous data
Bias-variance decomposition (BVD) is a powerful tool for understanding and improving data-driven models. It reveals sources of estimation errors. Existing literature has defined BVD for squared error but not absolute error, while absolute error is the more natural error metric and has shown advantag...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Elsevier
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8369249/ https://www.ncbi.nlm.nih.gov/pubmed/34430928 http://dx.doi.org/10.1016/j.patter.2021.100309 |