Prediction Error Decomposition
Prediction Error Decomposition is a theoretical framework in statistical learning that decomposes the expected prediction error of a model into constituent components attributable to bias, variance, and irreducible noise, thereby elucidating the tradeoffs governing model performance.
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6.1K
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117
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2017–2026
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Bias plus variance decomposition for zero-one loss functions
Ron Kohavi, David H. Wolpert · 1996 · 630 citations
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UNSW Sydney
Sydney, Australia
3 h-index
3 publications
1.4K citations

University of Liverpool
Liverpool, United Kingdom
2 h-index
2 publications
51 citations

Delft University of Technology
Delft, The Netherlands
2 h-index
6 publications
486 citations

Hohai University
Nanjing, China
2 h-index
2 publications
52 citations
Eindhoven University of Technology
Eindhoven, The Netherlands
2 h-index
3 publications
429 citations
Iowa State University
Ames, United States
2 h-index
2 publications
115 citations

Chengdu University of Technology
Chengdu, China
2 h-index
3 publications
325 citations

Université Paris Dauphine-PSL
Paris, France
2 h-index
2 publications
35 citations
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4 publications · 191 citations
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